ORIGINAL_ARTICLE
QDFSN: QoS-enabled Dynamic and Programmable Framework for SDN
Software Defined Network (SDN) can integrate a lot of network functions such as network resource management into a consolidated framework. TCP operates in these networks with low information traffic characteristics. As a result, it has to continuously change its congestion window size in order to handle drastic changes in the network or its traffic conditions. As a result, TCP frequently overshoots or undershoots its transmission rate, making it a native congestion control protocol. To overcome that problem, we have proposed a new QoS framework for SDN called QDFSN (QoS-enabled Dynamic and Programmable Framework for SDN) which can be effectively applied in Data Centers as well. In this, and by means of AQM (Active Queue Management), a new function for detecting the upcoming congestion situation is designed. In each node, a developed mathematical model is used to calculate the best parameters of the node adaptively, especially the service rate, to minimize the congestion in the network. This model is tested in many NS-2 scenarios, and the results are presented. The results show improvements in selected QoS parameters like throughput and delay. We conclude that QDFSN-based congestion control shortens the process of adapting TCP to network circumstances, and enhances the TCP performance.
https://tjee.tabrizu.ac.ir/article_13279_ed1fc1e794b4dc90ea1723a22dbc17b0.pdf
2021-04-21
1
10
SDN (SoftwareDefinedNetwork)
QoS (Quality of Service)
Data center
Y.
Darmani
darmani@kntu.ac.ir
1
Electrical Engineering Dept., K. N. Toosi University of Technology, Tehran, Iran
LEAD_AUTHOR
M.
Sangelaji
m.sangelaji@ee.kntu.ac.ir
2
Electrical Engineering Dept., K. N. Toosi University of Technology, Tehran, Iran.
AUTHOR
[1] M. Alizadeh, A. Kabbani, T. Edsall, B. Prabhakar, A. Vahdat, and M. Yasuda, “Less is more: trading a little bandwidth for ultra-low latency in the data center”, NSDI’12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation, 2012, San Jose, CA pp. 19–19.
1
[2] N. McKeown, T. Anderson, H. Balakrishnan, G. Parulkar, L. Peterson, J. Rexford, S. Shenker, and J. Turner, “OpenFlow: enabling innovation in campus networks”, ACM SIGCOMM Computer Communication Review, vol. 38, no. 2, pp. 69–74, 2008. DOI: 10.1145/1355734.1355746.
2
[3] V. Jacobson, “Congestion avoidance and control”, ACM SIGCOMM CCR, vol. 25, no. 1, pp. 157–187, 1995. DOI:10.1145/205447.205462.
3
[4] M. Alizadeh, A. Greenberg, D. Maltz, J. Padhye, P. Patel, B. Prabhakar, S. Sengupta, and M. Sridharan, “Data Center TCP (DCTCP)”, ACM SIGCOMM, 2010, New Delhi, India pp. 63–74. DOI:10.1145/1851182.1851192.
4
[5] Sandvine global Internet report. https://www.sandvine.com/hubfs/downloads/phenomena/2018-phenomena-report.pdf, Oct. 2018.
5
[6] T. Benson, A. Akella, and D. Maltz, “Network traffic characteristics of data centers in the wild”, Internet Measurement Conference, 2010, Melbourne, Australia pp. 267–280. DOI: 10.1145/1879141.1879175.
6
[7] Y. Chen, R. Griffith, J. Liu, R. H. Katz, and A. D. Joseph, “Understanding TCP incast throughput collapse in datacenter networks”, WREN, 2009, Barcelona, Spain pp. 73–82. DOI:10.1145/1592681.1592693.
7
[8] C. Jerry, “Tuning TCP Parameters for the 21st century”, http://www6.ietf.org/mail-archive/web/tcpm/current/msg04707.html.
8
[9] V. Vasudevan, A. Phanishayee, H. Shah, E. Krevat, D. G. Andersen, G. R. Ganger, G. A. Gibson, and B. Mueller, “Safe and effective fine-grained TCP retransmissions for datacenter communication”, SIGCOMM, 2009, Barcelona, Spain, pp. 303–314. DOI:10.1145/1592568.1592604.
9
[10] W. Cerroni, M. Garbaoei, “Cross-layer resource orchestration for cloud service delivery: A seamless SDN approach”, Computer Networks, vol. 87, pp. 16-32, 2015. DOI:10.1016/j.comnet.2015.05.008.
10
[11] B. Ahmadi; Z. Movahedi, “Stable Distributed Load Balancing between Controllers in Software Defined Networks”, Article 2, vol. 49, Issue 1 - Serial Number 87, Spring 2019, Page 13-23.
11
[12] Greenberg, G. Hjalmtysson, D. A Maltz, A. Myers, J. Rexford, G. Xie, H. Yan, J. Zhan, and H. Zhang, “A clean slate 4D approach to network control and management”, ACM SIGCOMM Computer Communication Review, 2005, vol. 35, no. 5, pp. 41-54. DOI:10.1145/1096536.1096541.
12
[13] Software Defined Networks (SDN) talk at Structure 2010. http://www.openflowswitch. org/wp/2010/07/software-defined-networks-sdn-talk-at-structure-2010/.
13
[14] H. Yan, D. A. Maltz, T. S. E. Ng, H. Gogineni, H. Zhang, and Z. Cai, “Tesseract: A 4D network control plane”, 4th Symposium on Networked Systems Design and Implementation, 2007, Cambridge, Massachusetts, USA.
14
[15] M. Casado, M. J. Freedman, J. Pettit, J. Luo, N. McKeown, and S. Shenker, “Ethane: taking control of the enterprise”, SIGCOMM '07 Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications, 2007, Kyoto, Japan vol. 37, no. 4, pp. 1–12. DOI:10.1145/1282380.1282382.
15
[16] M. Karakus, A. Durresi, “Quality of Service (QoS) in Software Defined Networking (SDN): A survey”, Journal of Network and Computer Applications, vol. 80, pp. 200-218, 2017. DOI:10.1016/j.jnca.2016.12.019.
16
[17] S. Floyd and V. Jacobson, “Random early detection gateways for congestion avoidance”, IEEE/ACM Transactions on Networking, vol. 1, no. 4, pp. 397–413, 1993. DOI:10.1109/90.251892.
17
[18] S. Floyd and K. Fall, “Router mechanisms to support end-to-end congestion control”, Technical report, 1997.
18
[19] D. Lin and R. Morris, “Dynamics of random early detection”, Proceedings of the ACM SIGCOMM '97 ?Conference on Applications, technologies, architectures, and protocols for computer communication, 1997, Cannes, France,vol. 27, no.4, pp. 127–137. DOI:10.1145/263105.263154.
19
[20] T. Ott Lakshman, T. V. Lakshman, and L. Wong, “Sred: Stabilized red”, IEEE INFOCOM '99, Conference on Computer Communications, New York, NY, USA,1999, pp. 1346–1355. DOI: 10.1109/INFCOM.1999.752153.
20
[21] R. Pan, B. Prabhakar, and K. Psounis, “Choke - a stateless active queue management scheme for approximating fair bandwidth allocation”, Proceedings IEEE INFOCOM 2000, Conference on Computer Communications, 1999. DOI: 10.1109/INFCOM.2000.832269.
21
[22] C. A. Grazia, N. Patriciello, M. Klapez and M. Casoni, “A cross-comparison between TCP and AQM algorithms: Which is the best couple for congestion control?”, 14th International Conference on Telecommunications (ConTEL), Zagreb, 2017, pp. 75-82. DOI: 10.23919/ConTEL.2017.8000042.
22
[23] S. Floyd, “TCP and explicit congestion notification”, ACM Computer Communication Review, vol. 24, no. 5, pp. 10–23, 1994. DOI:10.1145/205511.205512.
23
[24] J. Hong, C. Joo, and S. Bahk, “Active queue management algorithm considering queue and load states”, Proceedings, 13th International Conference on Computer Communications and Networks, Chicago, IL, USA, 2007, vol. 30, pp. 886–89. DOI: 10.1109/ICCCN.2004.1401608.
24
[25] J. W. Guck, A. Van Bemten, M. Reisslein and W. Kellerer, “Unicast QoS Routing Algorithms for SDN: A Comprehensive Survey and Performance Evaluation”, IEEE Communications Surveys & Tutorials, vol. 99, pp. 1-1, 2017. DOI: 10.1109/COMST.2017.2749760.
25
[26] P. Hongyu, W. Weidong, and W. Chaowei, “QoS-guaranteed energy saving routing strategy using SDN central control for backbone networks”, The Journal of China Universities of Posts and Telecommunications , vol. 22, no. 5, pp. 92-100, 2015. DOI:10.1016/S1005-8885(15)60686-0.
26
[27] J. Pang, G. Xu, X. Fu, K. Zhao, “Horizon: a QoS management framework for SDN-based data center networks”, Annals of Telecommunications, vol. 72, no. 1, pp. 597–605, 2017. DOI: 10.1007/s12243-017-0579-2.
27
[28] S. Jeong, D. Lee, J. Hyun, J. Li and J. W. K. Hong, “Application-aware traffic engineering in software defined network”, 19th Asia-Pacific Network Operations and Management Symposium (APNOMS), 2017, Seoul, Korea (South), pp. 315-318. DOI:10.1109/APNOMS.2017.8094144.
28
[29] A. Volkan Atli, M. Serkant Uluderya, S. Civanlar, B. Görkemli, A. Murat Tekalp, “TCP congestion avoidance for selective flows in SDN”, 26th Signal Processing and Communications Applications Conference (SIU), 2018. DOI: 10.1109/SIU.2018.8404643.
29
[30] A. M. Abdelmoniem, B. Bensaou, A. James Abu, “Mitigating incast-TCP congestion in data centers with SDN”, Annals of Telecommunications, April 2018, Volume 73, Issue 3–4, pp. 263–277.DOI:10.1007/s12243-017-0608-1.
30
[31] A. Kucminski, A. Al-Jawad, P. Shah and R. Trestian, “QoS-based routing over software defined networks”, IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2017, Cagliari, pp. 1-6. DOI: 10.1109/BMSB.2017.7986239.
31
[32] Shakthipriya P., Bevi A.R., “Network Protocol-Based QoS Routing Using Software Defined Networking. Artificial Intelligence and Evolutionary Computations in Engineering Systems”, Springer, Singapore, pp. 363-374, 2017. DOI:10.1007/978-981-10-3174-8_32.
32
[33] B. Siniarski, C. Olariu, P. Perry and J. Murphy, “OpenFlow based VoIP QoE monitoring in enterprise SDN”, IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Lisbon, 2017, pp. 660-663. DOI: 10.23919/INM.2017.7987354.
33
[34] M. Ghobadi, “TCP Adaptation Framework in Data Centers. Doctor of Philosophy, Graduate Department of Computer Science”, University of Toronto, 2013.
34
[35] A. Ghiasian, “Frequency scaling approach to reduce the power consumption of Openflow switches”, Tabriz Journal of Electrical Engineering, Volume 49, Issue 3 - Serial Number 89 , pp. 1273-1282 Autumn 2019.
35
ORIGINAL_ARTICLE
An area-efficient broadband balun-LNA-mixer front-end for multi-standard receivers
An area-efficient wideband receiver front-end for multi-standard receivers is presented. To handle large input signal levels, dual gain modes are employed in the LNA stage. For input signals lower than -17.5 dBm, a noise-canceling balun CG-CS LNA is employed. The LNA features a local feedback loop to reduce power consumption. For input signals in the range of from -17.5 to -5.2 dBm, the CG-CS LNA is bypassed with a balun unit-gain inverter stage. The proposed front-end shows better than -12 dB input matching for both gain modes in the frequency range of 0.4-3.4 GHz. Due to the lack of off-chip balun, the proposed front-end consumes low area. Moreover, the full differential structure leads to enhanced linearity performance. The post-layout simulation results in RF CMOS 0.18 µm process shows the conversion gain of 24.5/13.06 dB in HG/LG modes. The minimum DSB NF is 3.77/9.84 dB, and the third input intercept point (IIP3) is -7.29/-1.8 dBm. The circuit dissipates 12.93 mW with an active area of 0.073 mm2.
https://tjee.tabrizu.ac.ir/article_13283_ba1a283038d1219c9ad6fcb82accf5b1.pdf
2021-04-21
11
17
receiver front-end
balun LNA
low area
noise-canceling
wideband
inductor less
R.
Eskandari
ra_eskandari@sut.ac.ir
1
Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
AUTHOR
A.
Ebrahimi
aebrahimi@sut.ac.ir
2
Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
LEAD_AUTHOR
H.
Faraji
hfaraji@sut.ac.ir
3
Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
AUTHOR
[1] Gustafsson, M., et al., A low noise figure 1.2-V CMOS GPS receiver integrated as a part of a multimode receiver. IEEE Journal of Solid-State Circuits, 2007. 42(7): p. 1492-1500.
1
[2] Agnelli, F., et al., Wireless multi-standard terminals: system analysis and design of a reconfigurable RF front-end. IEEE Circuits and Systems Magazine, 2006. 6(1): p. 38-59.
2
[3] Tasic, A., W. Serdijn, and J. Long, Adaptive multi‐standard circuits and systems for wireless communications. IEEE Circuits and Systems Magazine, 2006. 6(1): p. 29‐37.
3
[4] Pärssinen, A. System design for multi-standard radios. IEEE ISSCC Girafe Forum, 2006.
4
[5] Yi, X., et al. A 65nm CMOS carrier-aggregation transceiver for IEEE 802.11 WLAN applications. IEEE Radio Frequency Integrated Circuits Symposium (RFIC), 2016.
5
[6] Hu, B., X. Yu, and L. He. A Gm-boosted and current peaking wideband merged LNA and mixer. IEEE International Conference on Ultra-Wideband, 2010.
6
[7] Ho, S.S. and C.E. Saavedra, A CMOS broadband low-noise mixer with noise cancellation. IEEE Transactions on Microwave Theory and Techniques, 2010. 58(5): p. 1126-1132.
7
[8] Darabi, H., et al., A 2.4-GHz CMOS transceiver for Bluetooth. IEEE Journal of Solid-State Circuits, 2001. 36(12): p. 2016-2024.
8
[9] Javidan, J. and S. Fazel, 0.9/2.4 GHz Active Switch Concurrent Dual-Band Power Amplifier Design in 0.18 μmRF CMOS. TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, 2016. 46(4): p. 85-94 (in persian).
9
[10] Bijari, A. and M. Sheikhi, A 3.1-10.6 GHz Ultra-Wideband Low Noise Amplifier with Novel Input Matching Network. TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, 2019. 49(2): p. 517-529(in persian).
10
[11] Bastos, I., et al., Noise canceling LNA with gain enhancement by using double feedback. INTEGRATION, the VLSI journal, 2016. 52: p. 309-315.
11
[12] Blaakmeer, S.C., et al., Wideband balun-LNA with simultaneous output balancing, noise-canceling and distortion-canceling. IEEE Journal of Solid-State Circuits, 2008. 43(6): p. 1341-1350.
12
[13] Abdelghany, M.A., et al., A low flicker noise direct conversion receiver for IEEE 802.11 g wireless LAN using differential active inductor. Microelectronics Journal, 2011. 42(2): p. 283-290.
13
[14] Fatin, G.Z., M.S. Oskooei, and Z.K. Kanani. A technique to improve noise figure and conversion gain of cmos mixers. 50th Midwest Symposium on Circuits and Systems, 2007.
14
[15] Darabi, H. and J. Chiu, A noise cancellation technique in active RF-CMOS mixers. IEEE Journal of Solid-State Circuits, 2005. 40(12): p. 2628-2632.
15
[16] Karrari, H., E.N. Aghdam, and H.F. Baghtash, A wide-band noise-cancelling direct-conversion balun-LNA-mixer front-end. Analog Integrated Circuits and Signal Processing, 2018. 96(1): p. 67-78.
16
[17] El-Desouki, M.M., et al., Toward Realization of 2.4 GHz Balunless Narrowband Receiver Front-End for Short Range Wireless Applications. Sensors, 2015. 15(5): p. 10791-10805.
17
[18] Miao, P., et al., A transformer-loaded receiver front end for 2.4 GHz WLAN in 0.13 μm CMOS technology. Journal of Semiconductors, 2011. 32(12): p. 125002.
18
ORIGINAL_ARTICLE
Time Resource Management in Cognitive Radar Using Adaptive Waveform Design
Cognitive Radar is a recently presented research topic, in which most efforts has been done for its conceptual description and the adaptive waveform design feature of these radars, while other aspects of additivity for optimum performance of cognitive radars has been ignored. In this paper, a framework for adaptive time resource management in Cognitive Radars is proposed. The main purpose of this paper is proposing an algorithm for time resource management, with incorporation of adaptive waveform design capability of cognitive radars, to enhance the radar performance for an efficient time resource usage. After developing the equations of radar time resource management using adaptive waveform design, an implementable algorithm is proposed for this purpose and its performance is simulated and analysed. The results show that the proposed algorithm resulted in more efficient time resource management compared to the existing ones.
https://tjee.tabrizu.ac.ir/article_13331_fa5df61c2add9c15608a10549b470174.pdf
2021-04-21
19
26
cognitive radar
radar resource management
adaptive waveform design
radar target tracking
M.
Ghadian
ghadian@mut.ac.ir
1
Electrical Engineering Faculty, Malek Ashtar Technical University, Tehran, Iran
AUTHOR
R.
Fatemi Mofrad
fatemi@mut.ac.ir
2
Electrical Engineering Faculty, Malek Ashtar Technical University, Tehran, Iran.
LEAD_AUTHOR
B.
Abbasi Arand
abbasi@modares.ac.ir
3
Electrical Engineering Faculty, Tarbiat Modares University, Tehran, Iran
AUTHOR
[1] B. Gillespie, E. Hughes, and M. Lewis, “Scan scheduling of multi-function phased array radars using heuristic techniques”, In IEEE International Radar Conference, May 2005, Arlington, USA, pp. 513–518.
1
[2] S. Haykin, “Cognitive Radar: a way of the future”, IEEE Signal Processing Magazine, vol. 23, no. 1, pp. 30-40, 2006.
2
[3] S. Haykin, Y. Xue, M. P. Setoodeh, “Cognitive radar: step toward bridging the gap between neuroscience and engineering”, Proceedings of IEEE, vol. 100, no. 11, pp. 3102-3130, 2012.
3
[4] S. Haykin, A. Zia, I. Arasaratnam, Y. Xue, “Cognitive tracking radar”, In IEEE Radar Conference, May 2010, Washington DC, USA, pp. 1467-1470.
4
[5] M. S. Greco, F. Gini, P. Stinco, “Cognitive radars: some applications”, In Global Conference on Signal and Information Processing, December 2016, Washington DC, USA, pp. 1077-1082.
5
[6] M. Greco, F. Gini, P. Stinco, K. Bell, “Cognitive radars: on the road to reality”, IEEE Signal Processing Magazine, vol. 35, no. 4, pp. 112-125, 2018.
6
[7] B. Jin, J. Gou, B. Su, D. He, Z. Zhang, “Adaptive Waveform Selection For Manoeuvring Target Tracking in Cognitive Radar”, Elsevier Digital Signal Processing, vol. 75, pp. 210-221, 2018.
7
[8] A. E. Mitchell, G. E. Smith, K. L. Bell, A. Duly, “Fully Adaptive Radar Cost Function Design”, In IEEE Radar Conference (Radar Conf. 2018), April 2018, Oklahoma City USA, pp. 1301-1306.
8
[9] X. Le, “Waveform Design for Tracking Systems”, In International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, September 2015, Xi'an China, pp. 458-462.
9
[10] K. L. Bell, C. J. Baker, G. E. Smith, J. T. Johnson, M. Rangaswamy, “Cognitive Radar Framework for Target Detection and Tracking”, IEEE Journal of Selected Topics in Signal Processing, vol. 9, iss. 8, pp. 1427-1439, 2015.
10
[11] N. H. Nguyen, K. Dogancay, L.M. Davis, “Adaptive Waveform Selection For Target Tracking In Clutter By Multi-static Radar System”, IEEE Transactions On Aerospace And Electronic Systems, vol. 51, no. 1, pp. 688-701, 2015.
11
[12] ابراهیم باقری، محمد حسین کهایی، محمد جباریان، علی اصغر بهشتی، “طراحی شکل موج ارسالی رادار به منظور بالا بردن دقت تخمین تاخیر و فرکانس داپلر هدف”، مجله مهندسی برق دانشگاه تبریز، جلد ۴۶، شماره ۳، پاییز ۱۳۹۵.
12
[13] N. H. Nguyen, K. Dogancay, L.M. Davis, “Adaptive Waveform Scheduling For Target Tracking In Clutter By Multi-static Radar System”, In IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), May 2014, Florence, Italy, pp. 1449-1453
13
[14] X. Wu, Z. Liu, R. Xie, X. Mu, “Adaptive Waveform Design for Enhanced Detection of Extended Target”, In CIE International Conference on Radar (RADAR), October 2016, Guangzhou China.
14
[15] M. Ghadian, R. Fatemi Mofrad, B. Abbasi Arand, “Designing Adaptive Time Resource Management Cost Function for Cognitive Radar”, IET Radar, Sonar & Navigation, 2020.
15
[16] M. Shaghaghi, R. Adve, Z. Ding, “Multifunction Cognitive Radar Task Scheduling Using Monte Carlo Tree Search and Policy Networks”, IET Radar, Sonar & Navigation, vol. 12, pp. 1437-1447, 2018.
16
[17] بهروز صفری نژادیان، مجتبی اسد، “ارائه دو فیلتر کالمن مرتبه کسری جدید برای سیستم های مرتبه کسری خطی در حضور نویز اندازه گیری رنگی”، مجله مهندسی برق دانشگاه تبریز، جلد ۴۷، شماره ۲، تابستان ۱۳۹۶.
17
[18] M. Orton, W. Fitzgerald, “A Bayesian Approach to Tracking Multiple Targets Using Sensor Arrays and Particle Filters”, IEEE Transactions On Signal Processing, vol. 50, no. 2, pp. 216-223, 2002.
18
[19] R. E. Kalman, “A New Approach to Linear Filtering and Prediction Problems”, Transactions of the ASME–Journal of Basic Engineering, pp. 35-45, 1960.
19
[20] P. J. Costa, “Adaptive Model Architecture and Extended Kalman – Bucy Filters”, IEEE Transactions on Aerospace Electronic Systems, vol. 30, pp. 525–533, 1994.
20
[21] E.A. Wan, R.v.d. Merwe, “The Unscented Kalman filter For Nonlinear Estimation”, in Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium, October. 2000, Alberta, Canada.
21
[22] K. Ito, K. Xiong, “Gaussian Filters for Nonlinear Filtering Problems”, IEEE Transactions On Automatic Control, vol. 45, no. 5, pp. 910-927, 2000.
22
[23] M. Sanjeev Arulampalam, S. Maskell, N. Gordon, T. Clapp, “A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking”, IEEE Transactions in Signal Processing, vol. 50, no. 2, pp. 174-188, 2002.
23
[24] S. Wang, D. Bi, H. Ruan, S. Chen, “Cognitive Structure Adaptive Particle Filter for Radar Manoeuvring Target Tracking”, IET Radar, Sonar & Navigation, vol. 13, pp. 23-30, 2019.
24
[25] R. Merwe, N. de Freitas, A. Doucet, E. Wan, “The Unscented Particle filter”, Technical Report CUED/FINFENG/TR380, Engineering Department, Cambridge University, 2000.
25
[26] S. Julier, J. Uhlmann, H.F. Durrant-Whyte, “A New Method for the Nonlinear Transformation of Means and Covariance In filters And Estimators”, IEEE Transactions on autumn. Control, pp. 477–482, 2000.
26
[27] N. Cui, L. Hong, J. Layne, “A Comparison of Nonlinear filtering approaches with an application to ground target tracking”, Elsevier Signal Processing, vol. 85, pp. 1469-1492, 2005.
27
[28] M. Steck, C. Neumann, M. Bockmair, “Cognitive Radar Principles and Application to Interference Reduction”, In the 19th International Radar Symposium IRS, June 2018, Bonn Germany.
28
[29] D. Kershaw, J. Evans, “Optimal Waveform Selection for Tracking Systems”, IEEE Transactions On Information Theory, vol. 40, no. 5, pp. 1536-1550, September 1994.
29
[30] L. U. Medina, J. Grajal, “Implementation of the fully adaptive radar framework: practical limitations”, IEEE Radar Conference (Radar Conf. 2017), May 2017, Seattle USA, pp. 761-766.
30
[31] G. Van Keuk, S. S. Blackman, “On phased array radar tracking and parameter control”, IEEE Transactions on Aerospace and Electronic Systems, vol. 29, no.1, pp. 186-194, 1993.
31
[32] J. H. Zwanga, Y. Boers, H. Driessen, “On Tracking Performance Constrained MFR Parameter Control”, Sixth International Conference of Information Fusion, July 2005, Queensland, Australia, pp. 712-718.
32
[33] H.J. Shin, S.N. Hong, D.H. Hong, "Adaptive Update Rate target tracking for Phased Array Radar. IEE Proceedings - Radar, Sonar Navigation, vol. 142, no. 3, pp. 137-143. 1995.
33
ORIGINAL_ARTICLE
Alpha particle spectrometry with CMOS webcam and a SBC
We present the development of a system for the detection and energy spectrometry of alpha particle radiation based on the Commercial Off The Shelf (COTS) CMOS image sensor. The data is read, processed and displayed in real-time using a single-board-computer (SBC). We show that by using image processing techniques, a slightly modified webcam can be used to measure α radiation. In contrast to dedicated measurement devices such as Geiger counters, our framework can classify the type of radiation and can differentiate between various kinds of ionizing radiation. The system was tested with standard Ra and Am alpha sources.
https://tjee.tabrizu.ac.ir/article_13340_d8bf0f4400030e7645df7cea0370bc9f.pdf
2021-04-21
27
32
Webcam
CMOS image sensor
Alpha particle
single-board-computer (SBC)
Image processing
R.
Hashemzadeh
rhashem@tabrizu.ac.ir
1
Faculty of Physics, University of Tabriz
AUTHOR
S.
Ashrafi
ashrafi@tabrizu.ac.ir
2
Faculty of Physics, University of Tabriz
LEAD_AUTHOR
H.
Naghshara
naghshara@tabrizu.ac.ir
3
Faculty of Physics, University of Tabriz
AUTHOR
H.
Kasani
hadi.kasani@tabrizu.ac.ir
4
Faculty of Physics, University of Tabriz
AUTHOR
[1] Adachi, S., Tokuda, S. and Ishida, S., 1996. A CMOS detector readout front-end for X-ray digital radiography systems. IEEE Transactions on Nuclear Science, 43(1), pp.249-255.
1
[2] Lane, D.W., 2012. X-ray imaging and spectroscopy using low cost COTS CMOS sensors. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 284, pp.29-32.
2
[3] Castoldi, A., Guazzoni, C., Maffessanti, S., Montemurro, G.V. and Carraresi, L., 2015. Commercial CMOS image sensors as X-ray imagers and particle beam monitors. Journal of Instrumentation, 10(01), p.C01002.
3
[4] Castoldi, A., Guazzoni, C., Maffessanti, S., Montemurro, G.V. and Carraresi, L., 2015. Application of naked commercial CMOS sensors to X-ray fluorescence and X-ray beam monitoring. In 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) (pp. 1-3). IEEE.
4
[5] Haro, M.S., Bessia, F.A., Pérez, M., Blostein, J.J., Balmaceda, D.F., Berisso, M.G. and Lipovetzky, J., 2020. Soft X-rays spectroscopy with a commercial CMOS image sensor at room temperature. Radiation Physics and Chemistry, 167, p.108354.
5
[6] Cogliati, J.J., Derr, K.W. and Wharton, J., 2014. Using CMOS sensors in a cellphone for gamma detection and classification. arXiv preprint arXiv:1401.0766.
6
[7] Gumiela, Michał & Kozik, Rafał. (2012). Studies of the applicability of CMOS and CCD sensors for detection, dosimetry and imaging of alpha, beta, gamma, X-ray and proton beam spots.
7
[8] Faruqi, A.R., Henderson, R., Pryddetch, M., Allport, P. and Evans, A., 2005. Direct single electron detection with a CMOS detector for electron microscopy. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 546(1-2), pp.170-175.
8
[9] Foran, B., Barnett, J., Lysaght, P.S., Agustin, M.P. and Stemmer, S., 2005. Characterization of advanced gate stacks for Si CMOS by electron energy-loss spectroscopy in scanning transmission electron microscopy. Journal of electron spectroscopy and related phenomena, 143(2-3), pp.149-158.
9
[10] S. Kumar, R. Prakash, S. Chouhan and A. K. Salhan, "CMOS sensors for microscopy, spectrophotometry and as a transducer in biosensors," 2013 IEEE Point-of-Care Healthcare Technologies (PHT), 2013, pp. 196-199, doi: 10.1109/PHT.2013.6461318.
10
[11] Pallone, A. and Newton, N., 2011, March. WASTED at work: the Webcam Alpha Spectrometer Experiment Demonstrator. In APS Meeting Abstracts.
11
[12] Fryman, J. and Pallone, A., 2012, February. The WASTED Resolutions: exploration of the spatial and energy limits of the Webcam Alpha Spectrometer TEchnology Demonstrator. In APS March Meeting Abstracts.
12
[13] Griffin, R.H., Le, H., Jack, D.T., Kochermin, A. and Tarr, N.G., 2008, October. Radon Monitor using Custom α-detecting MOS IC. In SENSORS, 2008 IEEE (pp. 906-909). IEEE.
13
[14] Griffin, R.H., Le, H., Jack, D.T. and Tarr, N.G., 2008, October. αRAM: An α particle detecting MOS IC for radon monitoring. In 2008 1st Microsystems and Nanoelectronics Research Conference (pp. 73-76). IEEE.
14
[15] Gumiela, Michał. (2014). Hardware random numbers generator based on Am-241 alpha decay.
15
[16] Griffin, R.H. and Tarr, N.G., 2013, October. Optical image sensors and their application in radon detection. In Photonics North 2013 (Vol. 8915, p. 89151C). International Society for Optics and Photonics.
16
[17] Knoll, G.F., 2010. Radiation detection and measurement. John Wiley & Sons.
17
[18] Mendis, S., Kemeny, S.E. and Fossum, E.R., 1994. CMOS active pixel image sensor. IEEE transactions on Electron Devices, 41(3), pp.452-453.
18
[19] El Gamal, A. and Eltoukhy, H., 2005. CMOS image sensors. IEEE Circuits and Devices Magazine, 21(3), pp.6-20.
19
[20] Bigas, M., Cabruja, E., Forest, J. and Salvi, J., 2006. Review of CMOS image sensors. Microelectronics journal, 37(5), pp.433-451.
20
[21] Yadid-Pecht, O. and Etienne-Cummings, R. eds., 2007. CMOS imagers: from phototransduction to image processing. Springer Science & Business Media.
21
[22] Ohta, J., 2007. Smart CMOS image sensors and applications. CRC press.
22
[23] Kuroda, T., 2014. Essential principles of image sensors. CRC press.
23
[24] Belredon, X., David, J.P., Lewis, D., Beauchene, T., Pouget, V., Barde, S. and Magnan, P., 2002. Heavy ion-induced charge collection mechanisms in CMOS active pixel sensor. IEEE Transactions on Nuclear Science, 49(6), pp.2836-2843.
24
[25] Pérez, M., Lipovetzky, J., Haro, M.S., Sidelnik, I., Blostein, J.J., Bessia, F.A. and Berisso, M.G., 2016. Particle detection and classification using commercial off the shelf CMOS image sensors. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 827, pp.171-180.
25
[26] PhysicsOpenlab @: http://physicsopenlab.org/2016/ 05/18/diy-webcam-particle-detector/
26
[27] L'Annunziata, M. F. (2012). Handbook of Radioactivity Analysis, Academic press. Waltham, USA.
27
[28] Auzinger, T., Habel, R., Musilek, A., Hainz, D., & Wimmer, M. (2012, August). GeigerCam: measuring radioactivity with webcams. In SIGGRAPH Posters (p. 40).
28
ORIGINAL_ARTICLE
Improving Coordination and Operating Speed of Overcurrent Relay against Contingency of Presence of Distributed Generators
The presence or absence of distributed generation (DG) sources in a distribution network has a probabilistic nature. In the event of connection or disconnection of these sources, the fault current through a relay and the relay operating time are affected, which leads to miscoordination. For solving this issue, coordination constraints corresponding to the presence or absence of DGs have to be considered in the overcurrent relay coordination problem (CP). The incorporation of these constraints increases the operating time (OT) of the conventional overcurrent relays (OCRs). In this paper, a novel adaptive characteristic is proposed to solve this unwanted effect. Accordingly, a function proportional to the equivalent impedance (EI) seen by the relay is added to the relay characteristic. This EI is calculated via in-situ measurement of voltage and current before the occurrence of a fault, continuously; when the fault occurs, the calculated impedance is used in the relay characteristic to determine the OT. The addition of this function to the conventional overcurrent relay characteristic, reduces the effects of disconnecting the DGs on the coordination constraint, and in general, improves the OT of the relay. Based on the analytical relations and simulation results, it is shown that the OTs of the primary and backup relays with the proposed characteristic are improved compared to the relays with the conventional characteristic.
https://tjee.tabrizu.ac.ir/article_13281_ede49d3f81322bfa18e706de6b59603a.pdf
2021-04-21
33
47
Adaptive Characteristic
contingency
Coordination
Distributed Generator (DG)
Equivalent Impedance (EI)
Overcurrent relay (OCR)
Nader
Hatefi Torshizi
n.hatefi@birjand.ac.ir
1
Department of Electrical Engineering, University of Birjand, Birjand, Iran.
AUTHOR
Hamidreza
Najafi
h_r_najafi@birjand.ac.ir
2
Department of Electrical Engineering, University of Birjand, Birjand, Iran.
AUTHOR
Abbas
Saberi Noghabi
a.saberi@birjand.ac.ir
3
Department of Electrical Engineering, University of Birjand, Birjand, Iran.
LEAD_AUTHOR
[1] A. Saberi Noughabi, H. Badrsimaei, and M. Farshad, "A Probabilistic Method to Determine the Optimal Setting of Combined Overcurrent Relays considering Uncertainties," (in en), TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, vol. 47, no. 1, pp. 141-153, 03/21 2017.
1
[2] D. S. Alkaran, M. R. Vatani, M. J. Sanjari, G. B. Gharehpetian, and A. H. Yatim, "Overcurrent relays coordination in interconnected networks using accurate analytical method and based on determination of fault critical point," IEEE Transactions on Power Delivery, vol. 30, no. 2, pp. 870-877, 2015.
2
[3] H. C. Kiliçkiran, İ. Şengör, H. Akdemir, B. Kekezoğlu, O. Erdinç, and N. G. Paterakis, "Power system protection with digital overcurrent relays: A review of non-standard characteristics," Electric Power Systems Research, vol. 164, pp. 89-102, 2018.
3
[4] G. Pepermans, J. Driesen, D. Haeseldonckx, R. Belmans, and W. D’haeseleer, "Distributed generation: definition, benefits and issues," Energy policy, vol. 33, no. 6, pp. 787-798, 2005.
4
[5] H. J. Monfared, A. Ghasemi, A. Loni, and M. Marzband, "A hybrid price-based demand response program for the residential micro-grid," Energy, vol. 185, pp. 274-285, 2019/10/15/ 2019.
5
[6] M. Marzband, F. Azarinejadian, M. Savaghebi, E. Pouresmaeil, J. M. Guerrero, and G. Lightbody, "Smart transactive energy framework in grid-connected multiple home microgrids under independent and coalition operations," Renewable Energy, vol. 126, pp. 95-106, 2018/10/01/ 2018.
6
[7] F. Katiraei and M. R. Iravani, "Power management strategies for a microgrid with multiple distributed generation units," IEEE transactions on power systems, vol. 21, no. 4, pp. 1821-1831, 2006.
7
[8] R. H. Lasseter, "MicroGrids," in 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309), 2002, vol. 1, pp. 305-308 vol.1.
8
[9] P. T. Manditereza and R. Bansal, "Renewable distributed generation: The hidden challenges–A review from the protection perspective," Renewable and Sustainable Energy Reviews, vol. 58, pp. 1457-1465, 2016.
9
[10] M. Singh, "Protection coordination in distribution systems with and without distributed energy resources-a review," Protection and Control of Modern Power Systems, vol. 2, no. 1, p. 27, 2017.
10
[11] A. Saberi Noughabi, "A New Index for Evaluating Distributed Generation Impacts on Overcurrent Relay Coordination," (in en), TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, vol. 46, no. 3, pp. 257-267, 09/22 2016.
11
[12] M. Ghotbi Maleki, R. Mohammadi, and H. Javadi, "Optimal Coordination of Overcurrent Relays Considering Generators Transient Currents," (in en), TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, vol. 49, no. 3, pp. 1249-1258, 12/01 2019.
12
[13] N. Jenkins, "Embedded generation. Part 1," Power engineering journal, vol. 9, no. 3, pp. 145-150, 1995.
13
[14] H. Yazdanpanahi, Y. W. Li, and W. Xu, "A new control strategy to mitigate the impact of inverter-based DGs on protection system," IEEE Transactions on Smart grid, vol. 3, no. 3, pp. 1427-1436, 2012.
14
[15] V. A. Papaspiliotopoulos, G. N. Korres, V. A. Kleftakis, and N. D. Hatziargyriou, "Hardware-in-the-loop design and optimal setting of adaptive protection schemes for distribution systems with distributed generation," IEEE Transactions on Power Delivery, vol. 32, no. 1, pp. 393-400, 2017.
15
[16] M. A. Mirzaei, A. S. Yazdankhah, B. Mohammadi-Ivatloo, M. Marzband, M. Shafie-khah, and J. P. S. Catalão, "Stochastic network-constrained co-optimization of energy and reserve products in renewable energy integrated power and gas networks with energy storage system," Journal of Cleaner Production, vol. 223, pp. 747-758, 2019/06/20/ 2019.
16
[17] H. Wan, K. Li, and K. Wong, "An adaptive multiagent approach to protection relay coordination with distributed generators in industrial power distribution system," IEEE Transactions on Industry Applications, vol. 46, no. 5, pp. 2118-2124, 2010.
17
[18] H. H. Zeineldin, H. M. Sharaf, D. K. Ibrahim, and E. E.-D. A. El-Zahab, "Optimal Protection Coordination for Meshed Distribution Systems With DG Using Dual Setting Directional Over-Current Relays," IEEE Trans. Smart Grid, vol. 6, no. 1, pp. 115-123, 2015.
18
[19] K. A. Saleh, H. H. Zeineldin, A. Al-Hinai, and E. F. El-Saadany, "Dual-setting characteristic for directional overcurrent relays considering multiple fault locations," IET Generation, Transmission & Distribution, vol. 9, no. 12, pp. 1332-1340, 2015.
19
[20] T. S. Aghdam, H. K. Karegar, and A. Abbasi, "Discussion on “Optimal Protection Coordination for Meshed Distribution Systems With DG Using Dual Setting Relays”," IEEE Transactions on Smart Grid, vol. 7, no. 3, pp. 1756-1756, 2016.
20
[21] H. H. Zeineldin, H. M. Sharaf, D. K. Ibrahim, and E. A. El-Zahab, "Closure to “Optimal Protection Coordination for Meshed Distribution Systems With DG Using Dual Setting Directional Over-Current Relays”," IEEE Transactions on Smart Grid, vol. 7, no. 3, pp. 1757-1757, 2016.
21
[22] V. C. Nikolaidis, E. Papanikolaou, and A. S. Safigianni, "A communication-assisted overcurrent protection scheme for radial distribution systems with distributed generation," IEEE transactions on smart grid, vol. 7, no. 1, pp. 114-123, 2016.
22
[23] M. N. Alam, "Adaptive Protection Coordination Scheme Using Numerical Directional Overcurrent Relays," IEEE Transactions on Industrial Informatics, vol. 15, no. 1, pp. 64-73, 2019.
23
[24] D. S. Kumar, D. Srinivasan, A. Sharma, and T. Reindl, "Adaptive directional overcurrent relaying scheme for meshed distribution networks," IET Generation, Transmission & Distribution, vol. 12, no. 13, pp. 3212-3220, 2018.
24
[25] M. Y. Shih, A. Conde, Z. Leonowicz, and L. Martirano, "An Adaptive Overcurrent Coordination Scheme to Improve Relay Sensitivity and Overcome Drawbacks due to Distributed Generation in Smart Grids," IEEE Transactions on Industry Applications, vol. 53, no. 6, pp. 5217-5228, 2017.
25
[26] M. Ojaghi and V. Mohammadi, "Use of Clustering to Reduce the Number of Different Setting Groups for Adaptive Coordination of Overcurrent Relays," IEEE Transactions on Power Delivery, vol. 33, no. 3, pp. 1204-1212, 2018.
26
[27] S. Chaitusaney and A. Yokoyama, "Prevention of reliability degradation from recloser–fuse miscoordination due to distributed generation," IEEE Transactions on Power Delivery, vol. 23, no. 4, pp. 2545-2554, 2008.
27
[28] W. El-Khattam and T. S. Sidhu, "Restoration of directional overcurrent relay coordination in distributed generation systems utilizing fault current limiter," IEEE Transactions on power delivery, vol. 23, no. 2, pp. 576-585, 2008.
28
[29] H. Ł, H. H. Zeineldin, and E. F. El-Saadany, "Protection Coordination Index Enhancement Considering Multiple DG Locations Using FCL," IEEE Transactions on Power Delivery, vol. 32, no. 1, pp. 344-350, 2017.
29
[30] K. Saleh, H. Zeineldin, A. Al-Hinai, and E. F. El-Saadany, "Optimal Coordination of Directional Overcurrent Relays Using a New Time–Current–Voltage Characteristic," Power Delivery, IEEE Transactions on, vol. 30, no. 2, pp. 537-544, 2015.
30
[31] S. Jamali and H. Borhani-Bahabadi, "Non-communication protection method for meshed and radial distribution networks with synchronous-based DG," International Journal of Electrical Power & Energy Systems, vol. 93, pp. 468-478, 2017.
31
[32] K. A. Saleh, H. H. Zeineldin, and E. F. El-Saadany, "Optimal protection coordination for microgrids considering N-1 contingency," IEEE Trans. Ind. Inform, vol. 13, pp. 2270-2278, 2017.
32
[33] A. S. Noghabi, H. R. Mashhadi, and J. Sadeh, "Optimal coordination of directional overcurrent relays considering different network topologies using interval linear programming," IEEE Transactions on Power Delivery, vol. 25, no. 3, pp. 1348-1354, 2010.
33
[34] E. Relay-Part, "3: single input energizing quantity measuring relay with dependent or independent time," IEC Standard, vol. 60255, no. 3, 1989.
34
[35] A. S. Noghabi, J. Sadeh, and H. R. Mashhadi, "Considering different network topologies in optimal overcurrent relay coordination using a hybrid GA," IEEE Transactions on Power Delivery, vol. 24, no. 4, pp. 1857-1863, 2009.
35
[36] J. M. Gers and E. J. Holmes, Protection of electricity distribution networks. IET, 2004.
36
[37] M. Ojaghi, Z. Sudi, and J. Faiz, "Implementation of full adaptive technique to optimal coordination of overcurrent relays," IEEE Transactions on Power Delivery, vol. 28, no. 1, pp. 235-244, 2013.
37
[38] S. Kar and S. R. Samantaray, "Time-frequency transform-based differential scheme for microgrid protection," IET Generation, Transmission & Distribution, vol. 8, no. 2, pp. 310-320, 2014.
38
ORIGINAL_ARTICLE
An Active, Low-Power, 10Gbps, Current-based Transimpedance Amplifier in a Broadband Optical Receiver Front-End
< p>< p class="TJEENormal">An integrated CMOS, low-power optical communication receiver front-end is designed and presented in this paper for specified applications of 10Gbp/s. The transimpedance amplifier (TIA) stage and the limiting amplifier (LA) stage possess an active feedforward network based on current-mirror topologies and differential topologies, respectively. In order to obtain broadband performance, low-power consumption characteristic and low-occupied area on chip, an active type of inductors are employed in the TIA as well as the LA stage. The performance of the optical system is simulated using 90 Nano-meter CMOS technology parameters, which exhibits power dissipation of only 1.5mW, -3dB frequency of 6.92GHz, 24pA/√Hz input referred noise, and transimpedance gain of 40.1dB ohm for the TIA stage, while, the whole optical receiver front-end consumes 7.7m Watt, providing 71.4dB ohm gain beside acquiring 6.55GHz frequency bandwidth. Finally, the performance of the presented optical receiver front-end as a low-power, 10Gbps block-diagram is justified.
https://tjee.tabrizu.ac.ir/article_13333_54bcabb5a032249932a3be33c649ef00.pdf
2021-04-21
49
60
Low-Power
Transimpedance amplifier
Limiting Amplifier
Optical Receiver
10Gbps
S. A.
Hosseinisharif
hosseinishariff@iauyazd.ac.ir
1
Department of Electrical Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran
AUTHOR
M.
Pourahmadi
pourahmadi@iauyazd.ac.ir
2
Department of Electrical Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran
LEAD_AUTHOR
M. R.
Shayesteh
shayestehh@iauyazd.ac.ir
3
Department of Electrical Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran
AUTHOR
[1] Y. Ota, R.G. Swartz, “Burst-mode compatible optical receiver with a large dynamic range”, J. Light. Technol., Vol. 8, pp. 1897–1903, 1990.
1
[2] B. Moeneclaey, J. Verbrugghe, F. Blache, M. Goix, D. Lanteri, B. Duval, J. Bauwelinck, X. Yin, “A 40-Gb/s transimpedance amplifier for optical links”, IEEE Photonics Technol. Lett., Vol. 27, pp. 1375–1378, 2015.
2
[3] B. Razavi, “Design of Integrated Circuit for Optical Comunications”, Second edition, John Wiley & Sons Inc, New Jersey, 2012.
3
[4] M. Rakideh, Seifouri, P. Amiri, “A folded cascode-based broadband transimpedance amplifier for optical communication”, Microelectron. J,.Vol. 54, pp. 1–8, 2016.
4
[5] S.M.R. Hasan, “Design of a low power 3.5-GHz broad-band CMOS”, IEEE Trans. Circuits Syst. I, Vol. 52, pp. 1061–1072, 2005.
5
[6] S. Zohoori, T. Shafiei, M. Dolatshahi, “A 274µW, Inductorless, Active RGC-Based Transimpedance Amplifier Operating at 5Gbps”, 27th Iranian Conference on Electrical Engineering (ICEE2019), pp. 1-4, 2019.
6
[7] R.Soltanisarvestani, S. Zohoori, A. soltanisarvestani, “A RGC-Based, Low-Power, CMOS Transimpedance Amplifier for 10Gb/s Optical Receivers”, International Journal of Electronics, Vol. 107, Issue. 3, 2020.
7
[8] S. Zohoori, M. Dolatshahi, “A Low-power, CMOS Transimpedance Amplifier in 90-nm technology for 5-Gbps optical communication applications”, International Journal of circuit theory and applications, Vol. 46, issue. 8, pp. 1-14, 2018.
8
[9] P. Amiri, M. Seifouri, B. Afarin, A. Hedayati Pour, “Design of RGC preamplifier with bandwidth 20GHz and transimpedance 60 dBΩ for telecommunication systems”, Tabriz, J. Electr. Eng., Vol. 46, pp. 15–23., 2016.
9
[10] D. Chen, S. Yeh, X. Shi, M.A. Do, C.C. Boon, W.M. Lim, “Cross-coupled current conveyor based CMOS transimpedance amplifier for broadband data transmission”, IEEE Trans. Very Large Scale Integer. (VLSI) Syst., Vol. 21, pp. 1516–1525, 2013.
10
[11] S. Zohoori, M. Dolatshahi, “A CMOS Low-Power Optical Front-End for 5Gbps Applications”, Fiber and Integrated Optics, Vol. 37, No. 1, pp. 37-56, 2018.
11
[12] M. Seifoui, P. Amiri, I. Dadras, “ An Electronic Transimpedance Amplifier for Optical Communications Network Based on Active Voltage-Current Feedback”, TABRIZ Journal of electrical Engineering, Vol. 48, No. 2, pp. 737-744, 2018. (in Persian)
12
[13] B. Analui, A. Hajimiri, “Bandwidth Expansion for transimpedance Amplifiers”, IEEE J. Solid-State Circuits, Vol. 39, pp. 1263–1270, 2004.
13
[14] S. Zohoori, M. Dolatshahi, “An inductor-less, 10Gbps Trans-impedance Amplifier Operating at low supply-voltage”, 25th Iranian conference on electrical Engineering (ICEE2017), pp. 145-148, 2017.
14
[15] S. Galal, B. Razavi, “40-Gb/s amplifier and esdprotection circuit in 0.18-μm CMOS technology”, IEEE J. Solid-State Circuits,Vol. 39, pp. 2389–2396, 2004.
15
[16] J. Park, D. J. Allstot, “A matrix amplifier in0.18μm SOI CMOS”, IEEE Trans. Circuits Syst, Vol. 53, pp. 561–568, 2006.
16
[17] L. Liu, J. Zou, N. Ma, Zh. Zhu, Y. Yang, “A CMOS Transimpedance Amplifier with high gain and wide dynamic range for optical fiber Sensing System”, Optik, Vol. 126, pp. 1389-1393, 2015.
17
[18] K. Monfared, Y. Belghisazar, “Improved Low Voltage Low Power Recycling Folded Fully Differential Cascode Amplifier”, TABRIZ Journal of electrical Engineering, Vol. 48, No. 1, pp. 327-334, 2018. (in Persian)
18
[19] B. Razavi, “Design of Analog CMOS Integrated Circuits”, MacGraw–Hill Series in Electrical and Computer Engineering, 2002.
19
[20] M. H. Taghavi, A. Naji, L. Belotstotski and J.W. Hasllet, “On the use of multi-path inductorless TIA for Larger Transimpedance limit”, Analog Integrated Circuit and Signal Processing, Vol. 77, No. 2 , 2013.
20
[21] W. Chen, Y. Cheng and D. Lin, “A 1.8v 10Gbps Fully Integrated CMOS Optical Receiver Analog Front End”, IEEE Journal of Solid State Circuits, Vol. 40 , pp. 3904-3907, 2007.
21
[22] M. Rakideh, M. Seifouri, P. Amiri, “A folded cascode-based broadband transimpedance amplifier for optical communication”, Microelectronics Journals. Vol. 54, pp. 1–8, 2016.
22
[23] D. Chen, S. Yeh, X. Shi, M.A. Do, C.C. Boon, W.M. Lim, “Cross-coupled current conveyor based CMOS transimpedance amplifier for broadband data transmission,” IEEE Transactions on Very Large Scale Integeratiobn (VLSI) System, Vol. 21, pp. 1516–1525, 2013.
23
[24] M. H. Taghavi, L. Belostotski, J.W. Haslett, P. Ahmadi, “10-Gb/s 0.13-μm CMOS inductor less modified-RGC transimpedance amplifier”, IEEE Transactions on Circuits and Systems, Vol. 62, pp. 1971–1980, 2015.
24
[25] P. Andre, S. Jacobus, “Design of a high gain and power efficient optical receiver front-end in 0.13μm RF CMOS technology for 10Gbps applications”, Microw. Opt. Technol. Lett., Vol. 58, pp.1499–1504, 2016.
25
[26] K. Honda, H. Katsurai, M. Nada, “A 56-Gb/s transimpedance amplifier in 0.13-μm SiGe BiCMOS for an optical receiver with −18.8dBm input sensitivity”, in: Proceeding of the IEEE Compound Semiconductor Integrated Circuit Symposium (CSICS), 2016.
26
[27] X. Hui, F. Jun, L. Quan and L. Wei, “A 3.125Gb/s Inductor-less Amplifier for Optical Communication in 0.35µm CMOS, Journal of Semiconductors”, Chinese Institute of electronics, Vol. 32, No. 10, pp. 105003_1-105003_5, 2011.
27
[28] M. Seifouri, P. Amiri, I. Dadras, “A transimpedance Amplifier for optical communication network based on active voltage-current feedback”, Microelectronics Journal, Vol. 67 , pp. 25-31, 2017.
28
[29] Y. Chen, J. Li, Z. Zhang, H. Wang, Y. Zhang, “12-Channel, 480 Gbit/s optical receiver analogue front-end in 0.13μm BiCMOS technology”, Electronics Letter, Vol. 53, pp. 492–494, 2017.
29
[30] R. Y. Chen, Z.Y. Yang, “CMOS transimpedance amplifier for gigabit-per-second optical wireless communications”, IEEE Transaction on Circuits and Systems II, Vol. 63, pp. 418–422, 2016.
30
[31] S. Zohoori, M. Dolatshahi, M. Pourahmadi, M. Hajisafari, “An Inverter-Based, CMOS, Low power Optical Receiver Front-End”, Fiber and Integrated Optics, Vol. 38, Issue. 1, pp. 1-19, 2019
31
ORIGINAL_ARTICLE
Reference spur suppression in the Integer-N frequency synthesizers by reducing periodic ripples amplitude on the VCO control voltage
To achieve a low reference spur for an Integer-N frequency synthesizer, a new spur reducing technique was proposed. To reduce the size of periodic ripples on the VCO control voltage, the low pass filter, and the charge pump were added with a spur reduction system. By lowering the amplitude of the periodic ripple on the VCO control voltage, we managed to lower the reference spur. The introduced technique removes the necessity to decrease bandwidth and CVO gain reference spur suppressing. To demonstrate the effectiveness of the proposed structure, a 2.06 – 2.22 GHz frequency synthesizer was used and the 180-nm CMOS technology was used for post-layout simulation. The proposed frequency synthesizer represents the reference spur of -85.84 dBc at 20 MHz offset and phase noise of -108dBc/Hz at 200 kHz offset frequency also it is locked after 2.8us while occupied 0.35 mm2 of the chip area.
https://tjee.tabrizu.ac.ir/article_13328_8c0add5e1f246ba6451fff5d4fe44065.pdf
2021-04-21
61
69
Spur suppression
reference spur
voltage controlled oscillator (VCO)
and integer-N frequency synthesizer
S.
Jahangirzadeh
jahangirzadeh.s@karoon.ac.ir
1
Department of Electrical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
A.
Amirabadi
a_amirabadi@azad.ac.ir
2
Department of Electrical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
A.
Farrokhi
ali_farrokhi@azad.ac.ir
3
Department of Electrical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.
LEAD_AUTHOR
[1] T.H. Lin, W. J. Kaiser, “A 900-MHz 2.5-mA CMOS frequency synthesizer with an automatic SC tuning loop”, IEEE Journal of Solid-State Circuits, vol. 36, no. 3, pp. 424-431, 2001.
1
[2] S. Pellerano, S. Levantino, C. Samori, A. L. Lacaita, “A 13.5-mW 5-GHz frequency synthesizer with dynamic-Logic frequency divider”, IEEE Journal of Solid-State Circuits, vol. 39, no. 2, pp. 378–383, 2004.
2
[3] F. Herzel, G. Fischer, H. Gustat, “An integrated CMOS RF synthesizer for 802.11a wireless LAN”, IEEE Journal of Solid-State Circuits, vol. 38, no. 10, pp. 1767–1770, 2003.
3
[4] X. Gao, E. Klumperink, G. Socci, M. Bohsali, B. Nauta, “Spur reduction techniques for phase-locked loops exploiting a sub-sampling phase detector”, IEEE Journal of Solid-State Circuits, vol. 45, no. 9, pp. 1809–1821, 2010.
4
[5] X. Li, J. Zhang, Y. Zhang, W. Wang, H. Liu, C. Lu, “A 5.7–6.0 GHz CMOS PLL with low phase noise and −68 dBc reference spur”, International Journal of Electronics and Communications, vol. 85, pp. 23–31, 2018.
5
[6] A. Kraal, F. Behbahani, A. A. Abidi, “RF-CMOS oscillators with switched tuning”, Proceedings of the IEEE 1998 Custom Integrated Circuits Conference, May 1998, Santa Clara, CA, USA, USA, pp. 555–558.
6
[7] D. Mandal, P. Mandal, T. K. Bhattacharyya, “Spur reducing architecture of frequency synthesizer using
7
switched capacitors”, IET Circuits Devices Systems, vol. 8, no. 4, pp. 237–245, 2014.
8
[8] S. Bruss, R. Spencer, “A 5-GHz CMOS type-II PLL with low KVCO and extended fine-tuning range”, IEEE Transactions on Microw, vol. 57, no. 8, pp. 1978–1988, 2009.
9
[9] C.Y. Kuo, J.Y. Chang, S. I. Liu, “A spur-reduction technique for a 5-GHz frequency synthesizer”, IEEE Transactions on circuits and systems, vol. 53, no. 3, pp. 526–533, 2006.
10
[10] C. M. Hung, K. O. Kenneth, “A fully integrated 1.5-V 5.5-GHz CMOS phase-locked loop”, IEEE Journal of Solid-State Circuits, vol. 37, no. 4, pp. 521–525, 2002.
11
[11] Z. Bereber, S. Kameche, E.Benlchelifa, “High tolerance of charge pump leakage current in Integer-N PLL frequency synthesizer for 5G networks”, Simulation Modelling Practice and Theory, vol. 95, pp. 134-147, 2019.
12
[12] W. B. Wilson, U. Moon, K. R. Lakshmikumar, L. Dai, “A CMOS self-calibrating frequency synthesizer”, IEEE Journal of Solid-State Circuits, vol. 35, no. 10, pp. 1437–1444, 2000.
13
[13] T. C. Lee, W. L. Lee, “A spur suppression technique for phase-locked Frequency synthesizers”, IEEE International Solid-State Circuits Conference (ISSCC), Feb 2006, Kuala Lumpur, Malaysia, pp. 2432-2433.
14
[14] H. Gan Ko, W. Bae, G. Jeong, D.kyoon, “Reference Spur Reduction Techniques for a Phase-Locked Loop,” IEEE Access, vol. 7, pp. 38035–38043, 2019.
15
[15] J. Sharma, H. Krishnaswamy, “A 2.4-GHz Reference-Sampling Phase-Locked Loop That Simultaneously Achieves Low-Noise and Low-Spur Performance”, IEEE Journal of Solid-State Circuits, vol. 45, no. 5, pp. 1407–1424, 2019.
16
[16] C. Thambidurai, N. Krishnapura, “On pulse position modulation and its application to PLLs for spur reduction”, IEEE Transactions on Circuits and Systems, vol. 58, no. 7, pp. 1483–1496, 2011.
17
[17] J. Choi, W. Kim, K. Lim, “A spur suppression technique using an edge interpolator for a charge-pump PLL”, IEEE Transactions on Circuits and Systems, vol. 20, no. 5, pp. 969–973, 2012.
18
[18] J. F. Huang, J. L. Yang, R.Y. Liu, “The 1-V 2.4 GHz low-spur Fractional-N frequency synthesizer chip design with exploiting randomly selected PFD and subsampling charge pump”, Microwave and Optical Technology Letters, vol. 57, no. 1, pp. 61-66, 2015.
19
[19] T.W. Liao, C. M. Chen, J. R. Su, C. C. Hung, “Random Pulse Width Matching Frequency Synthesizer with Sub-Sampling Charge Pump”, IEEE Transactions on Circuits and Systems, vol. 59, no. 12, pp. 2815– 2824, 2012.
20
[20] C. F. Liang, H. H. Chen, S. I. Liu, “Spur-suppression techniques for frequency synthesizers”, IEEE Transactions on Circuits and Systems, vol. 54, no. 8, pp. 653–657, 2007.
21
[21] M. Elsayed, A. Latif, E. Sinencio, “A Spur-Frequency-Boosting PLL with a -74 dBc Reference-Spur Suppression in 90 nm Digital CMOS”, IEEE Journal of Solid-State Circuits, vol. 48, no. 9, pp. 2104-2117, 2013.
22
[22] S. Jahangirzadeh, A. Amirabadi, A. Farrokhi, “Low spur frequency synthesizer using randomly shifted reference spur to higher frequencies,” International Journal of Electronics, 107 (12), 2044-2067, 2020.
23
[23] N. Xi, F. Lin, T. Ye, “A Low-Spur and Intrinsically Aligned IL-PLL with Self-Feedback Injection Locked RO and Pseudo-Random Injection Locked Technique”, IEEE Transactions on Circuits and Systems, pp.1-10, 2020.
24
[24] D. Biswas, G. S. Javed, K. S. Reddy, “5-GHz Integer-N PLL with spur reduction sampler”, IEEE Electronics Letters, vol. 55, no. 23, pp. 1217–1220, 2019.
25
[25] M. A. Karami, M. Ansarian, S. Aghli-Moghaddam, “A Novel Ring Voltage Controlled Oscillator utilizing Miller Effect”, Tabriz Journal of Electrical Engineering, vol. 47, no. 1, pp. 221-228, 2017.
26
[26] Abbas Nasri, Mostafa Yargholi, “Design of Class C VCO with Frequency Tripler for 17.8 – 19.19 GHz”, Tabriz Journal of Electrical Engineering, vol. 48, no. 4, pp. 1841-1852, 2018.
27
[27] N. Kamal, S. Al-Sahrawi, D. Abbott, “An accurate analytical spur model for an Integer-N phase-locked loop”, IEEE 4th International Conference on Intelligent and Advanced Systems (ICIAS), June 2012, Kuala Lumpur, Malaysia, pp. 659-664.
28
[28] Y.W. Chen, Y.H. Yu, Y. J. Emery Chen, “A 0.18- m CMOS dual-band frequency synthesizer with spur reduction calibration”, IEEE Microwave and Wireless Components Letters, vol. 23, no. 10, pp. 551–553, 2013.
29
ORIGINAL_ARTICLE
Identification and Determination of Contribution of Current Harmonics and Unbalanced in Microgrids Equipped with Advanced Metering Infrastructure
The use of distributed generation resources (grid-connected or islanded) such as solar systems and wind turbines in the form of microgrids can solve problems related to traditional power systems. On the other hand, the monitoring of power quality disturbances in microgrids is an important issue for compensating these problems. Among the various types of power quality disturbances, harmonic distortions are important. Accordingly, in this paper, a computational method has been used based on the recursive least squares withthe variable forgetting factor (VFF-RLS). The prominent features of the proposed method are its high accuracy and speed, as well as identification with a low rate of signal samples. The main aim of the proposed method is to identify the contribution and extent of harmonics and unbalanced in a microgrid equipped with Advanced Metering Infrastructure (AMI). In the proposed method, the identification is based on real-time estimation and using measured data with high computational speed and accuracy. The results of simulation by MATLAB software, and as well as the experimental results using the TMS320F2812 digital signal processor (DSP) show the validity of the proposed method.
https://tjee.tabrizu.ac.ir/article_13329_aa2d24c872b378565d79fb329c02d34c.pdf
2021-04-21
71
81
microgrid
power quality phenomena
advanced metering infrastructure
recursive least squares
identification of harmonic sources
contribution of harmonic level
H.
Joorabli
hjoorabli@outlook.com
1
Department of Electrical Engineering, Semnan Branch, Islamic Azad University, Semnan, Iran
AUTHOR
G. B.
Gharehpetian
grptian@aut.ac.ir
2
Department of Electrical Engineering, Faculty of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
LEAD_AUTHOR
S.
Ghasemzadeh
g_zadeh@tabrizu.ac.ir
3
Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, Tabriz University, Tabriz, Iran
AUTHOR
V.
Ghods
v.ghods@semnaniau.ac.ir
4
Department of Electrical Engineering, Semnan Branch, Islamic Azad University, Semnan, Iran
AUTHOR
[1] سامان درویش کرمانی، محمود جورابیان، گئورک قرهپتیان، «معماری ریزشبکههای با نقاط اتصال چندگانه به چندین شبکه و یا ریزشبکههای دیگر»، مجله مهندسی برق دانشگاه تبریز، دوره 48، شماره 3، صفحه 1105-1115، پاییز 1397.
1
[2] G. Yang-yang, C. Zhi-yuan, Z. Qing-song, Z. Guan-feng, “New reactive islanding detected method for microgrid”, In Power System Technology (POWERCON) IEEE International Conference on, November2016, Wollongong, Australia, pp. 1-6.
2
[3] R. Majumder, “A hybrid microgrid with DC connection at back to back converters”, IEEE Transactions on Smart Grid , vol. 5, no. 1, pp. 251-259, 2014.
3
[4] S.C. Huang, C.N. Lu, Y.L. Lo, “Evaluation of AMI and SCADA data synergy for distribution feeder modelling”, IEEE Transactions on Smart Grid, vol. 6, no. 4, pp. 1639-1647, 2015.
4
[5] Y. Wang, H. Qiu, Y. Tu, Q. Liu, Y. Ding, W. Wang, “A Review of Smart Metering for Future Chinese Grids”, Energy Procedia, vol. 152, pp. 1194-1199, 2018.
5
[6] J.L. Rueda, C.A. Juárez, I. Erlich, “Wavelet-based analysis of power system low-frequency electromechanical oscillations”, IEEE Transactions on Power Systems, vol. 26, no. 3, pp. 1733-1743, 2011.
6
[7] X.H. Peng, Q. Zhou, X.Y. Cao, “A high precision combinational optimization algorithm of power grid harmonic/inter-harmonic signal detection”, Power System Protection and Control, vol. 42, no. 23, pp. 95-101, 2014.
7
[8] A.K. Broen, M. Amin, E. Skjong, M. Molinas, “Instantaneous frequency tracking of harmonic distortions for grid impedance identification based on Kalman filtering”, In Control and Modeling for Power Electronics (COMPEL), IEEE 17th Workshop on, September 2016, Trondheim, Norway, pp. 1-7.
8
[9] A. Yazdaninejadi, A. Hamidi, S. Golshannavaz, F. Aminifar, S. Teimourzadeh, “Impact of inverter-based DERs integration on protection, control, operation, and planning of electrical distribution grids”, The Electricity Journal, vol. 32, no. 6, pp. 43-56, 2019.
9
[10] O. Homaee, A. Zakariazadeh, S. Jadid, “Real time voltage control using emergency demand response in distribution system by integrating ,advanced metering infrastructure”, Journal of Renewable and Sustainable Energy, vol 6, no. 3, pp. 033145, 2014.
10
[11] H. Chen, Y. He, J. Xiao, M. Liu, D. Wang, “Harmonics detection based on a combination of continuous wavelet transform and discrete wavelet transform”, Power System Protection and Control, vol. 43, no. 20, pp. 71-75, 2015.
11
[12] X. Tang, K. M. Tsang, W. L. Chan, “A power quality compensator with DG interface capability using repetitive control”, IEEE Transactions on Energy Conversion, vol. 27, no. 2, pp. 213-219, 2012.
12
[13] A. K. Sharma, O. P. Mahela, S. R. Ola, “Detection of power quality disturbances using discrete wavelet transform”, In 5th International Conference on Electrical Engineering-Boumerdes (ICEE-B), May2017, Bhopal, India, pp. 1-5.
13
[14] Y. Du, L. Du, B. Lu, R. Harley, T. Habetler, “A review of identification and monitoring methods for electric loads in commercial and residential buildings”, In Energy Conversion Congress and Exposition (ECCE) IEEE, November 2010, Atlanta, USA, pp. 4527-4533.
14
[15] R. R. Mohassel, A. S. Fung, F. Mohammadi, K. Raahemifar, “A survey on Advanced Metering Infrastructure”, Electrical Power and Energy Systems, vol. 63, pp. 473-484, 2014.
15
[16] R. Cisneros-Magaña, A. Medina, V. Dinavahi, A. Ramos-Paz, “Time-Domain Power Quality State Estimation Based on Kalman Filter Using Parallel Computing on Graphics Processing Units”, IEEE Access,vol. 6, pp. 21152-21163, 2018.
16
[17] J. Malvar, Ó. López, A. G. Yepes, A. Vidal, F. D. Freijedo, P. Fernández-Comesaña, J. Doval-Gandoy, “Graphical diagram for subspace and sequence identification of time harmonics in symmetrical multiphase machines”, IEEE Transactions on Industrial Electronics, vol. 61, no. 1, pp. 29-42, 2014.
17
[18] رضا باقری، جواد شکرالهی مغانی، گئورک قره پتیان، «جداسازی سهم مشترک و شبکه در اغتشاشات هارمونیکی بر مبنای یک مدل بهبودیافته»، مجله امیرکبیر، دوره 39، شماره 1، صفحه 35-45، بهار و تابستان 1387.
18
[19] M. Farhoodnea, A. Mohamed, H. Shareef, H. Zayandehroodi, “An enhanced method for contribution assessment of utility and customer harmonic distortions in radial and weakly meshed distribution systems”, International Journal of Electrical Power & Energy Systems, vol. 43, no. 1, pp. 222-229, 2012.
19
[20] D. Vujatovic, K. Leong Koo, Z. Emin, “Methodology of calculating harmonic distortion from multiple traction loads”, Electric Power Systems Research, vol. 138, pp. 165-171, 2016.
20
[21] M. I. Marei, E. F. El-Saadany, M. M.A. Salama, “A flexible DG interface based on a new RLS algorithm for power quality improvement”, IEEE Systems Journal, vol 6, no. 1, pp. 68-75, 2012.
21
[22] مریمالسادات اخوان حجازی، جواد ابراهیمی، مریم صباغپورآرانی، گئورک قرهپتیان، «تشخیص برخط عیبهای مکانیکی سیم پیچ ترانسفورماتور با استفاده از تخمین تابع تبدیل کانال انتشار موج UWB»، مجله مهندسی برق دانشگاه تبریز، دوره 47، شماره 4، صفحه 1307-1315، زمستان 1396.
22
[23] C. Xiangwen, C. Xiaoke, Y. Jianhua, W. Zhuo, Z. Jinquan, “A PLS-SVM-based method of general single-phase harmonic load identification”, In 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), June 2018, Guilin, China, pp. 102-105.
23
[24] A. Moradifar, A. Akbari Foroud, M. Fouladi, “Identification of multiple harmonic sources in power system containing inverter-based distribution generations using empirical mode decomposition”, IET Generation, Transmission & Distribution, vol. 13, no. 8, pp. 1401-1413, 2019.
24
[25] سینا نظری، سعید اسماعیلی، فرزاد کریمزاده، «شناسایی و دسته بندی اغتشاشات تکی و ترکیبی کیفیت توان با استفاده از روشی مبتنی بر تحلیل مؤلفه های مستقل»، مجله مهندسی برق دانشگاه تبریز، دوره 48، شماره 1، صفحه 381-392، بهار 1397.
25
ORIGINAL_ARTICLE
A New Fault-Tolerant Control of Wind Turbine Pitch System Based on ANN Model and Robust and Optimal Development of MRAC Method
In this paper, a new method is provided for Fault-Tolerant Control (FTC) of wind turbine pitch systems. One of the common faults in wind turbines is the defects of the pitch sub-system. Each blade of wind turbines tracks a reference signal; it is generated by the main controller unit, defects of actuators, or disturbance decrease of the reference signal quality. Classic controllers cannot deal with the disturbance and compensate for the faults to maintain system performance in normal operating conditions. For this purpose, a novel method based on Optimal Robust Model Reference Adaptive Control (ORMRAC) is presented, the output of the proposed method is a new adaptive rule. The ORMRAC method is robust, optimal, and fast at the same time. The proposed structure includes Fault Detection (FD) and FTC units. FD acts based on the generation and evaluation of residuals. The residual generation is based on Artificial Neural Network (ANN) model. When there is disturbance or fault in the pitch system and residual exceeds the certain threshold, the FT unit is activated. The proposed FT method is tested and evaluated using a wind turbine simulator based on practical data. The results indicated the proper performance of the proposed method in comparison with conventional MRAC and some other methods.
https://tjee.tabrizu.ac.ir/article_13282_9aeccd977926d36152194e433c0b3631.pdf
2021-04-21
83
95
Pitch angle
wind turbine (WT)
MRAC
ORMRAC
Fault Tolerant
ANN
M.
Kamarzarrin
kamarzarrin.mehrnoosh@sru.ac.ir
1
Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
AUTHOR
M. H.
Refan
refan@sru.ac.ir
2
Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
LEAD_AUTHOR
Adel
Dameshghi
a.dameshghi@sru.ac.ir
3
Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
AUTHOR
[1] A. Dameshghi, MH. Refan, “A new strategy for short-term power-curve prediction of wind turbine based on PSO-LS-WSVM”, IJEEE, vol. 14, no. 4, pp. 392-403, 2018.
1
[2] M. Rahimi, M.R. Esmaeili, “Power controller design and damping improvement of torsional oscillations in the 710 kW DFIG based wind turbine installed at the Binalood site,” Tabriz Journal of Electrical Engineering, vol. 46, no. 4, pp. 123-134, 2 (In persian).
2
[3] A. Dameshghi, M.H. Refan, P. Amiri, “Wind turbine doubly fed induction generator rotor electrical asymmetry detection based on an adaptive least mean squares filtering of wavelet transform”, Wind Engineering, vol. 10, no. 3, pp. 11-22, 2019.
3
[4] A. Dameshghi, M.H. Refan, “Combination of condition monitoring and prognosis systems based on current measurement and PSO-LS-SVM method for wind turbine DFIGs with rotor electrical asymmetry”, Energy Systems, pp. 1-30, 2019.
4
[5] Z. Jiang, M. Karimirad, T. Moan, “Dynamic response analysis of wind turbines under blade pitch system fault, grid loss, and shutdown events”, Wind Energy, vol. 17, no. 9, pp. 1385-1409, 2014.
5
[6] Y. Shabboei, A. Rikhtegari, S. Khanmohammadi, “Design of Fault Tolerant Nonsingular Terminal Sliding Mode Control for Nonlinear Systems based on an Adaptive Extended Kalman Filter,” vol. 46, no. 4, pp. 173-183, 2016 (In persian).
6
[7] H. Schulte, E. Gauterin, “Fault-tolerant control of wind turbines with hydrostatic transmission using Takagi–Sugeno and sliding mode techniques”, Annual Reviews in Control, vol. 40, pp. 82-92, 2015.
7
[8] J. Lan, R. J. Patton, X. Zhu, “Fault-tolerant wind turbine pitch control using adaptive sliding mode estimation”, Renewable Energy, vol. 116, pp. 219-231, 2018.
8
[9] H. Badihi, Y. Zhang, H, Hong, “Fault-tolerant cooperative control in an offshore wind farm using model-free and model-based fault detection and diagnosis approaches”, Appl Energ, vol. 201, pp. 284-307, 2016.
9
[10] L. Jalali, M. R. Nezhad-Ahmadi, M. Gohari, P. Bigelow, S. McColl, “The impact of psychological factors on self-reported sleep disturbance among people living in the vicinity of wind turbines”, Environmental Research, vol. 148, pp. 401-410, 2016.
10
[11] H. Badihi, Y. Zhang, H. Hong, “Model-based Active Fault-tolerant Cooperative Control in an Offshore Wind Farm”, Energy Procedia, vol. 103, pp. 46-51, 2016.
11
[12] E. B. Muhando, T. Senjyu, A. Uehara, “Gain-Scheduled H∞ Control for WECS via LMI Techniques and Parametrically Dependent Feedback Part II: Controller Design and Implementation”, IEEE T IND ELECTRON, vol. 58, no. 1, pp. 57-68, 2010.
12
[13] M. Mirzaei, H. H, Niemann, N. K. Poulsen, “A μ-synthesis approach to robust control of a wind turbine”, 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), 2011, Orlando, FL.
13
[14] M. Mirzaei, M. Soltani, K. Poulsen, H. H. Niemann, “An MPC approach to individual pitch control of wind turbines using uncertain LIDAR measurements”, European Control Conference (ECC), Zurich INSPEC. 2013. Accession Number: 13950748.
14
[15] M. L. Corradini, G. Ippoliti, G. Orlando, “Robust Control of Variable-Speed Wind Turbines Based on an Aerodynamic Torque Observer”, IEEE T CONTR SYST T, vol. 21, no. 4, pp. 1199-1206, 2013.
15
[16] L. Danyong, S. Yongduan, C. Wenchuan, H. Karimi, “Wind Turbine Pitch Control and Load Mitigation Using an L1 Adaptive Approach”, Mathematical Problems in Engineering, 2014.
16
[17] D. Corcuera, A. Pujana, A. Ezquerra, M. Segurola, E. Landaluze, “H∞ Based Control for Load Mitigation in Wind Turbines”, Energies, vol. 5, no. 4, pp. 938-967, 2012.
17
[18] L. L. Fan, Y. D, Song, “Neuro-Adaptive Model-Reference Fault- Tolerant Control with Application to Wind Turbines”, IET Control Theory & Applications, vol. 6, no. 4, pp. 475-486, 2012.
18
[19] E. Kamal, A. Aitouche, R. Ghorbani, M. Bayart, “Robust Fuzzy Fault-Tolerant Control of Wind Energy Conversion Systems subject to Sensor Faults”, IEEE Transactions on Sustainable Energy, vol. 3, no. 2, pp. 231-241, 2012.
19
[20] C. Sloth, T. Esbensen, J. Stoustrup, “Robust and Fault-Tolerant Linear Parameter-Varying Control of Wind Turbines”, Mechatronics, vol. 21, no. 4, pp. 645-659, 2011.
20
[21] H. Badihi, Y. M. Zhang, H. Hong, “Fuzzy Gain-Scheduled Active Fault-Tolerant Control of a Wind Turbine”, J FRANKLIN I, vol. 351, no. 7, pp. 3677-3706, 2014.
21
[22] S. Simani, P. Castaldi, “Data–Drive Design of Fuzzy Logic Fault Tolerant Control for a Wind Turbine Benchmark”, IFAC Proceedings, vol. 45, no. 20, pp. 108-113, 2012.
22
[23] S. Simani, S. Farsoni, P. Castaldi, “Fault-Tolerant Control of an Offshore Wind Farm via Fuzzy Modelling and Identification”, IFAC-Papers OnLine, vol. 48, no. 21, pp. 1345-1350, 2015.
23
[24] H. Badihi, Y. Zhang, H. Hong, “Model Reference Adaptive Fault-Tolerant Control for a Wind Turbine against Actuator Faults”, Conference on Control and Fault-Tolerant Systems (SysTol), October 2013 Nice, France.
24
[25] Y. Vidal, Ch. Tutivén, J. Rodellar, L. Acho, “Fault Diagnosis and Fault-Tolerant Control of Wind Turbines via a Discrete Time Controller with a Disturbance Compensator”, Energies, vol. 8, no. 5, pp. 4300-4316, 2015.
25
[26] F. Shi, R. Patton, “An active fault tolerant control approach to an offshore wind turbine model”, Renewable Energy, vol. 75, pp. 788-798, 2014.
26
[27] S. Cho, Z. Gao, T. Moan, “Model-based fault detection, fault isolation and fault-tolerant control of a blade pitch system in floating wind turbines”, Renewable Energy, vol. 120, pp. 306-321, 2018.
27
[28] H. Badihi, Y. Zhang, S. Rakheja, P. Pillay, “Model-Based Fault-Tolerant Pitch Control of an Offshore Wind Turbine”, IFAC-PapersOnLine, vol. 51, no. 18, pp. 221-226, 2018.
28
[29] H. Badihi, Y. Zhang, “Fault-tolerant individual pitch control of a wind turbine with actuator faults”, IFAC-PapersOnLine, vol. 51, no. 24, pp. 1133-1140, 2018.
29
[30] J. Lan, R. J. Patton, X. Zhu, “Fault-tolerant wind turbine pitch control using adaptive sliding mode estimation”, Renewable Energy, vol. 116, pp. 219-231, 2018.
30
[31] Y. Liu, R. J. Patton, J. Lan, J. “Fault-tolerant Individual Pitch Control using Adaptive Sliding Mode Observer”, IFAC-PapersOnLine, vol. 51, no. 24, pp. 1127-1132, 2018.
31
[32] S. Mullick, “Design of Model Reference Adaptive Controller with Multiplicative Structured Uncertainty”, Msc Thesis, Jadavpur University, 2012.
32
[33] J. Nelson, M. J Balas, R. S Erwin, “Model Reference Adaptive Control of Mildly Non-Linear Systems with Time Varying Input Delays – Part I”, Advances in Aerospace Guidance, Navigation and Control, 2013.
33
[34] K.S Narendra, M. Annaswamy, “A new adaptive law for robust adaptation without persistent excitation”, IEEE T AUTOMAT CONTR, vol. 32, no. 2, pp. 134-145, 1987.
34
[35] T. Nguyen, “Optimal control modification for robust adaptive control with large adaptive gain”, Syst Control Lett, vol. 61, no. 4, pp. 485-494, 2012.
35
[36] T. Zhang, S. S Ge, C. C. Hang, “Adaptive control of first-order systems with nonlinear parameterization”, IEEE T AUTOMAT CONTR, vol. 45, no. 8, pp. 1512-1516, 2000.
36
[37] P.A. Ioannou, V. Kokotovic, “Instability analysis and improvement of robustness of adaptive control”, Automatica, vol. 20, no. 5, pp. 583-594, 1984.
37
[38] K. Khalil, J. W. Grizzle, “Nonlinear systems,” vol. 3. New Jersey: Prentice hall, 1996.
38
[39] G. Cybenko, “Approximation by superpositions of a sigmoidal function”, MCSS, vol. 2, no. 4, pp. 303-314, 1989.
39
[40] C.A. Micchelli, “Interpolation of scattered data: distance matrices and conditionally positive definite functions”, Constructive Approximation, vol. 2, no. 1, pp. 11-22, 1986.
40
[41] A.J. Calise, T. Yucelen, “Adaptive Loop Transfer Recovery”, JGCD, vol. 35, no. 3, pp. 807-815, 2012.
41
[42] Mapna 2.5 MW (Mapna Group) Wind Turbine available at [http://www.thewindpower.net/turbine_en_986_mapna-group_2500.php]
42
[43] F. A. Inthamoussou, F. B Bianchi, R. J. Mantz, “LPV Wind Turbine Control with Anti-Windup Features Covering the Complete Wind Speed Range”, IEEE T Energy Conver, vol. 29, no.1, pp. 259-266, 2014.
43
ORIGINAL_ARTICLE
A Hybrid Meta-Heuristic Algorithm for High Performance Computing
Regarding optimization problems, there is a high demand for high-performance algorithms that can process the problem solution-space efficiently and find the best ones quite quickly. An approach to get this target is based on using swarm intelligence algorithms; these algorithms apply a population of simple agents to communicate locally with one another and with their surroundings. In this paper, we propose a novel approach based on combining the characteristics of the two algorithms: Cat Swarm Optimization (CSO) and the Shuffled Frog Leaping Algorithm (SFLA). The experimental results show the convergence ratio of our hybrid SFLA-CSO algorithm is seven times higher than that of CSO and five times higher than the convergence ratio of the standard SFLA algorithm. The obtained results also revealed that the hybrid method speeds up the convergence significantly, and reduces the error rate. We compared the proposed hybrid algorithm against the famous relevant algorithms PSO, ACO, ABC, GA, and SA; the results are valuable and promising.
https://tjee.tabrizu.ac.ir/article_13332_926792ee9fa739c23b17bfcc6de98f3b.pdf
2021-04-21
97
107
Cat swarm optimization
Convergence rate
Shuffled frog leaping algorithm
Swarm Intelligence
E.
Mahdipour
elham.mahdipour@stu.yazd.ac.ir
1
Computer Engineering Department, Yazd University, Yazd, Iran.
AUTHOR
M.
Ghasemzadeh
m.ghasemzadeh@yazd.ac.ir
2
Computer Engineering Department, Yazd University, Yazd, Iran.
LEAD_AUTHOR
[1] S.Nejatian, R.Omidvar, H.Parvin, V. Rezaei, M.Yasrebi, “A new algorithm: wild mice colony algorithm (WMC)”, Tabriz Journal of Electrical Engineering, vol. 49, no. 1, pp. 425-437, 2019 (in Persian).
1
[2] A. Afroughinia and R. Kardehi Moghaddam, “Competitive learning:A new metaheuristic optimization algorithm”, International Journal on Artificial Intelligence Tools, vol. 27, no. 08, p. 1850035, 2018.
2
[3] M. Mohammadpour, H. Parvin, “Chaotic genetic algorithm based on clustering and memory for solving dynamic optimization problems”, Tabriz Journal of Electrical Engineering, vol.46, no.3, pp. 299-318, 2016 (in Persian).
3
[4] C.-W. Tsai, W.-Y. Chang, Y.-C. Wang and H. Chen, “A high-performance parallel coral reef optimization for data clustering”, Soft Computing, vol. 23, no. 19, pp. 1-14, 2019.
4
[5] X. Nie, W. Wang, H. Nie, “Chaos quantum-behaved cat swarm optimization algorithm and its application in the PV MPPT”, Computational Intelligence and Neuroscience, vol. 2017, 2017.
5
[6] Wang L, Gong Y, “A fast shuffled frog leaping algorithm”, In Ninth International Conference on Natural Computation (ICNC), July 2013, IEEE, pp. 369-373.
6
[7] M. M. Eusuff and K. E. Lansey, “Optimization of water distribution network design using the shuffled frog leaping algorithm”, Journal of Water Resources planning and management, vol. 129, no. 3, pp. 210-225, 2003.
7
[8] S.-C. Chu, P.-W. Tsai and J.-S. Pan, “Cat swarm optimization”, In Pacific Rim International Conference on Artificial Intelligence, August 2006, Springer, Berlin, Heidelberg, pp. 854-858.
8
[9] C. Jingcao, R. Zhou, and D. Lei, “Dynamic shuffled frog-leaping algorithm for distributed hybrid flow shop scheduling with multiprocessor tasks”, Engineering Applications of Artificial Intelligence, vol. 90, p. 103540, 2020.
9
[10] T. Jianxin, R. Zhang, P. Wang, Z. Zhao, L. Fan, and X. Liu, “A discrete shuffled frog-leaping algorithm to identify influential nodes for influence maximization in social networks” Knowledge-Based Systems, vol. 187, p. 104833, 2020.
10
[11]A. Kaveh, S. Talatahari and N. Khodadadi, “Hybrid invasive weed optimization-shuffled frog-leaping algorithm for optimal design of truss structures”, Iranian Journal of Science and Technology, Transactions of Civil Engineering, pp. 1-16, 2019.
11
[12] R. Dash, R. Dash and R. Rautray, “An evolutionary framework-based microarray gene selection and classification approach using binary shuffled frog leaping algorithm”, Journal of King Saud University-Computer and Information Sciences, 2019.
12
[13] R. Dash, “Performance analysis of a higher order neural network with an improved shuffled frog leaping algorithm for currency exchange rate prediction”, Applied Soft Computing, vol. 67, pp. 215-231, 2018.
13
[14] T. K. Sharma and M. Pant, “Identification of noise in multi noise plant using enhanced version of shuffled frog leaping algorithm”, International Journal of System Assurance Engineering and Management,vol. 9, no. 1, pp. 43-51, 2018.
14
[15] K. Daoden and T. Thaiupathump, “Applying shuffled frog leaping algorithm and bottom left fill algorithm in rectangular packing problem”, In 2017 7th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC), July 2017, IEEE, pp. 136–139.
15
[16] P. Kaur and S. Mehta, “Resource provisioning and work flow scheduling in clouds using augmented shuffled frog leaping algorithm”, Journal of Parallel and Distributed Computing, vol. 101, pp. 41–50, 2017.
16
[17] D. Lei, Y. Zheng and X. Guo, “A shuffled frog leaping algorithm for flexible job shop scheduling with the consideration of energy consumption”, International Journal of Production Research, vol. 55, no. 11, pp. 3126–3140, 2017.
17
[18] R. Chompu-Inwai and T. Thaiupathump, “Optimal cost driver selection in activity- based costing using shuffled frog leaping algorithm”, Proceedings of the International Conference on Industrial Engineering and Operations Management, April 2017, Rabat, Morocco.
18
[19] Villa, Trinidad Castro, and Oscar Castillo. “Adaptation of Parameters with Binary Cat Swarm Optimization Algorithm of Controller for a Mobile Autonomous Robot”, In Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine, Springer, Cham, pp. 35-46, 2020.
19
[20] M. Shahid Ali, A. Ahmad, J. Shafique, “Integer cat swarm optimization algorithm for multiobjective integer problems”, Soft Computing, vol. 24, no. 3, pp. 1927-1955, 2020.
20
[21] R. Soto, B. Crawford, A. Aste Toledo, C. Castro, F. Paredes, R. Olivares et al., “Solving the manufacturing cell design problem through binary cat swarm optimization with dynamic mixture ratios”, Computational Intelligence and Neuroscience, vol. 2019, 2019.
21
[22] L.Pappula, D. Ghosh, “Cat swarm optimization with normal mutation for fast convergence of multimodal functions”, Applied Soft Computing, vol. 66, pp. 473–491, 2018.
22
[23] M. Zhao, “A novel compact cat swarm optimization based on differential method”, Enterprise Information Systems,vol. 14, no. 2, pp. 196-220, 2020.
23
[24] A. Thomas, P. Majumdar, T. Eldho, A. Rastogi, “Simulation optimization model for aquifer parameter estimation using coupled meshfree point collocation method and cat swarm optimization”, Engineering Analysis with Boundary Elements, vol. 91, pp. 60–72, 2018.
24
[25] V. I. Skoullis, I. X. Tassopoulos, G. N. Beligiannis, “Solving the high school timetabling problem using a hybrid cat swarm optimization based algorithm”, Applied Soft Computing, vol. 52, pp. 277–289, 2017.
25
[26] H. Chen, Q. Feng, X. Zhang, S. Wang, W. Zhou, Y. Geng, “Well placement optimization using an analytical formula-based objective function and cat swarm optimization algorithm”, Journal of Petroleum Science and Engineering, vol. 157, pp. 1067–1083, 2017.
26
[27] B. Kumar, M. Kalra, P. Singh, “Discrete binary cat swarm optimization for scheduling workflow applications in cloud systems”, In 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT), February 2017, IEEE, pp.1–6.
27
[28] D. Diana, “Novel cat swarm optimization algorithm to enhance channel equalization”, COMPEL- The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 36, no. 1, pp. 350–363, 2017.
28
[29] S.-H. Wang, W. Yang, Z. Dong, P. Phillips, Y.-D. Zhang, “Facial emotion recognition via discrete wavelet transform, principal component analysis, and cat swarm optimization”, In International Conference on Intelligent Science and Big Data Engineering, September 2017, Springer, Cham, pp. 203–214.
29
[30] E. N. Kencana, N. Kiswanti, K. Sari, “The application of cat swarm optimization algorithm in classifying small loan performance”, Journal of Physics: Conference Series, vol. 893, no. 1, IOP Publishing, p. 012037, 2017.
30
[31] D. Gabi, A. S. Ismail, A. Zainal, Z. Zakaria, A. Al-Khasawneh, “Cloud scalable multi-objective task scheduling algorithm for cloud computing using cat swarm optimization and simulated annealing”, In 2017 8th International Conference on Information Technology (ICIT), May 2017, IEEE, pp. 599–604.
31
[32] K. K. Dhaliwal, J. S. Dhillon, “Integrated cat swarm optimization and differential evolution algorithm for optimal IIR filter design in multi-objective framework”, Circuits, Systems, and Signal Processing, vol. 36, no. 1, pp. 270–296, 2017.
32
[33] M. Jamil, X.-S. Yang, “A literature survey of benchmark functions for global optimization problems”, arXiv preprint arXiv:1308.4008, 2013.
33
ORIGINAL_ARTICLE
A Novel Multi-objective Particle Swarm Algorithm Based on a Neighborhood to Search Depth in Task Scheduling by Considering a New Security Model
Cloud computing is a novel technology that provides users with better opportunities to gain access to services on the Internet. Users should utilize organizational services to meet their needs. They can also benefit from non-organizational services with high capacity but limited security. This study aims to provide a new security model that addresses security requirements for tasks and data as well as security strength for resources and communication paths. The proposed security model is defined security distance concept. Minimizing security distance has to do with task scheduling so that the resources can be matched with the security level and the data will be fitted into the appropriate communication path. The proposed scheduling algorithm takes the server profit into account in addition to the minimum security distance. The increased server profits can lead to higher resource sharing by the servers. The proposed scenario is implemented based on a neighborhood to search depth in task scheduling. This algorithm utilizes a novel ‘far and near neighborhood’ approach to select the best particle position. The approach generates both diversity and convergence in the set of answers. Finally, the proposed algorithm is compared with three other similar scheduling algorithms obtained by VNPSO, MPSO and NSGAII, considering the security of the cloud computing environment. The computational results show the effectiveness of the proposed algorithm to obtain resources with similar security and higher server profits.
https://tjee.tabrizu.ac.ir/article_13280_778276f709236bab91985bec000c3893.pdf
2021-04-21
109
119
Task scheduling Security requirement Security strength Security distance Multi
objective particle swarm optimization
Maedeh
Mehravaran
m.mehravaran@stu.yazd.ac.ir
1
Faculty of Computer Engineering, Yazd University, Yazd, Iran.
AUTHOR
Fazlollah
Adibnia
fadib@yazd.ac.ir
2
Faculty of Computer Engineering, Yazd University, Yazd, Iran.
LEAD_AUTHOR
Mohammad-Reza
Pajoohan
pajoohan@yazd.ac.ir
3
Faculty of Computer Engineering, Yazd University, Yazd, Iran.
AUTHOR
[1] C. Jianfang, C. Junjie, and Z. Qingshan, "An optimized scheduling algorithm on a cloud workflow using a discrete particle swarm," Cybernetics and Information Technologies, vol. 14, pp. 25-39, 2014.
1
[2] M. Naghibzadeh, "Modeling Workflow of Tasks and Task Interaction Graphs to Schedule on the Cloud," CLOUD COMPUTING 2016, p. 81, 2016.
2
[3] M. Yazdanbakhsh and R. Khorsand, "A Task Scheduling Strategy to Improve Qualitative Features in the Cloud Computing Environment," Tabriz Journal of Electrical Engineering, vol. 49, pp. 1427-1437, 2019 (in persian).
3
[4] L. Singh and S. Singh, "A survey of workflow scheduling algorithms and research issues," International Journal of Computer Applications, vol. 74, 2013.
4
[5] R. Gupta, "Above the Clouds: A View of Cloud Computing," Asian Journal of Research in Social Sciences and Humanities, vol. 2, pp. 84-110, 2012.
5
[6] T. Ghafari and S. Bakhtiari Chehelcheshmeh, "Secure Outsourcing Cloud Data using Lattice-based Secret Sharing," Tabriz Journal of Electrical Engineering, vol. 49, pp1211-1221,2019(in persian).
6
[7] H.Abrishami, A.Rezaeian, M.Naghibzadeh, "Scheduling in hybrid cloud to maintin data privacy", 20th National CSI Computer Conference, 2015. (In Persian)
7
[8] H. Liu, A. Abraham, V. Snášel, and S. McLoone, "Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments," Information Sciences, vol. 192, pp. 228-243, 2012.
8
[9] W. Liu, S. Peng, W. Du, W. Wang, and G. S. Zeng, "Security-aware intermediate data placement strategy in scientific cloud workflows," Knowledge and information systems, vol. 41, pp. 423-447, 2014.
9
[10] H. Chen, X. Zhu, D. Qiu, L. Liu, and Z. Du, "Scheduling for workflows with security-sensitive intermediate data by selective tasks duplication in clouds," IEEE Transactions on Parallel and Distributed Systems, 2017.
10
[11] Z. Li, J. Ge, H. Yang, L. Huang, H. Hu, H. Hu, et al., "A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds," Future Generation Computer Systems, 2016.
11
[12] M. L. Pinedo, "Scheduling: theory, algorithms, and systems", Springer International Publishing, 2016.
12
[13] F. Wu, Q. Wu, and Y. Tan, "Workflow scheduling in cloud: a survey," The Journal of Supercomputing, vol. 71, pp. 3373-3418, 2015.
13
[14] M. Masdari, S. ValiKardan, Z. Shahi, and S. I. Azar, "Towards workflow scheduling in cloud computing: a comprehensive analysis," Journal of Network and Computer Applications, vol. 66, pp. 64-82, 2016.
14
[15] W. Chen and E. Deelman, "Workflowsim: A toolkit for simulating scientific workflows in distributed environments," in 8th IEEE International Conference on E-science , pp. 1-8, 2012.
15
[16] H. Abrishami, A. Rezaeian, and M. Naghibzadeh, "Workflow Scheduling on the Hybrid Cloud to Maintain Data Privacy under Deadline Constraint," Journal of Intelligent Computing Volume, vol. 6, p. 93, 2015.
16
[17] H. Abrishami, A. Rezaeian, and M. Naghibzadeh, "A novel deadline-constrained scheduling to preserve data privacy in hybrid Cloud," in 5th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 234-23, 2015.
17
[18] S. Sharif, J. Taheri, A. Y. Zomaya, and S. Nepal, "Mphc: Preserving privacy for workflow execution in hybrid clouds," in International Conference on Parallel and Distributed Computing, Applications and Technologies, pp. 272-280, 2013.
18
[19] S. Abrishami, M. Naghibzadeh, and D. H. Epema, "Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds," Future Generation Computer Systems, vol. 29, pp. 158-169, 2013.
19
[20] D. Fernández-Cerero, A. Jakóbik, D. Grzonka, J. Kołodziej, and A. Fernández-Montes, "Security supportive energy-aware scheduling and energy policies for cloud environments," Journal of Parallel and Distributed Computing, vol. 119, pp. 191-202, 2018.
20
[21] Y. Wen, J. Liu, W. Dou, X. Xu, B. Cao, and J. Chen, "Scheduling workflows with privacy protection constraints for big data applications on cloud," Future Generation Computer Systems, 2018.
21
[22] E. S. Alkayal, N. R. Jennings, and M. F. Abulkhair, "Efficient task scheduling multi-objective particle swarm optimization in cloud computing," in 41st IEEE Conference on Local Computer Networks Workshops (LCN Workshops), pp. 17-24, 2016.
22
[23] K. Pradeep and T. P. Jacob, "CGSA scheduler: A multi-objective-based hybrid approach for task scheduling in cloud environment," Information Security Journal: A Global Perspective, vol. 27, pp. 77-91, 2018.
23
[24] V. Arabnejad, K. Bubendorfer, and B. Ng, "Scheduling deadline constrained scientific workflows on dynamically provisioned cloud resources," Future Generation Computer Systems, Vol.75, pp. 348-364, 2017.
24
[25] N. Chopra and S. Singh, "HEFT based workflow scheduling algorithm for cost optimization within deadline in hybrid clouds," in Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), pp. 1-6, 2013.
25
[26] G. Kaur and M. Kalra, "Deadline constrained scheduling of scientific workflows on cloud using hybrid genetic algorithm," in 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence, pp. 276-280, 2017.
26
[27] Z. Fan, T. Wang, Z. Cheng, G. Li, and F. Gu, "An Improved Multiobjective Particle Swarm Optimization Algorithm Using Minimum Distance of Point to Line," Shock and Vibration, vol. 2017
27
[28] R. Fan, L. Wei, X. Li, and Z. Hu, "A novel multi-objective PSO algorithm based on completion-checking," Journal of Intelligent & Fuzzy Systems, vol. 34, pp. 321-333, 2018.
28
[29] B. Jana, M. Chakraborty, and T. Mandal, "A Task Scheduling Technique Based on Particle Swarm Optimization Algorithm in Cloud Environment," in Soft Computing: Theories and Applications, ed: Springer, pp. 525-536. 2019.
29
[30] A. S. Kumar and M. Venkatesan, "Multi-Objective Task Scheduling Using Hybrid Genetic-Ant Colony Optimization Algorithm in Cloud Environment," Wireless Personal Communications, vol. 107, pp. 1835-1848, 2019.
30
[31] B. Keshanchi, A. Souri, and N. J. Navimipour, "An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing," Journal of Systems and Software, vol. 124, pp. 1-21, 2017.
31
[32] G. Juve, A. Chervenak, E. Deelman, S. Bharathi, G. Mehta, and K. Vahi, "Characterizing and profiling scientific workflows," Future Generation Computer Systems, vol. 29, pp. 682-692, 2013.
32
[33] P. S. Naidu and B. Bhagat, "Secure workflow scheduling in cloud environment using modified particle swarm optimization with scout adaptation," International Journal of Modeling, Simulation, and Scientific Computing, vol. 9, p. 1750064, 2018.
33
[33] N. Sooezi, S. Abrishami, and M. Lotfian, "Scheduling Data-Driven Workflows in Multi-cloud Environment," in 2015 IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom), pp. 163-167, 2015
34
[35] M. Naghibzadeh, "Modeling and scheduling hybrid workflows of tasks and task interaction graphs on the cloud," Future Generation Computer Systems, vol. 65, pp. 33-45, 2016.
35
ORIGINAL_ARTICLE
Design and Simulation of a Slice-Rail with Multi Projectile and Coaxial Railguns using 2D-FEM
Railguns has been researched considerably in recent years. Most of these researches is done to improve the main features of railgun, such as, increment of gradient of inductance L′, more uniform current density distributions, and launch synchronously multi projectiles per shot. In this paper, first the slice-rail railgun is presented and simulated by ANSYS software. Then, double and quad slice-rail with one axial is presented for multi-projectile shooting. Finally, the complete case of this slice-rail structure is studied as coaxial railgun. The geometry of slice-rail railgun has inner rail radii (Ri) and width (R1), outer rail radii (Ro) and width (R2) and the total angle of curved rails (θ). Current density distribution, Magnetic flux density and inductance gradient are computed for slice and coaxial railgun. Magnetic field at the outside of the muzzle for slice railgun with θ =90˚ is computed and compared with rectangular railgun meanwhile L′ equals to 0.45 μH/m for both railguns.
https://tjee.tabrizu.ac.ir/article_13284_d0c3256ce00b6dc8230d662dcdb9e5ab.pdf
2021-04-21
121
127
Coaxial Railgun
current distribution
finite element method
inductance gradient
multi-projectile
Shahab
Mozafari
sh11011sh@gmail.com
1
Department of Electrical Engineering Razi University, Kermanshah, Iran
AUTHOR
M.
Sajjad Bayati
s.bayati@razi.ac.ir
2
Department of Electrical Engineering Razi University, Kermanshah, Iran
LEAD_AUTHOR
[1] O. Bozic, P. Giese, “Aerothermodynamics Aspects of Railgun-Assisted Launches of Projectiles with Sub- and Low-Earth-Orbit Payloads,” IEEE Transactions on Magnetics, vol. 43, no. 1, pp. 474-479, 2007.
1
[2] I. R. McNab, “Progress on hypervelocity railgun research for launch to space,” IEEE Transactions on Magnetics, vol. 45, no. 1, pp. 381–388, 2009.
2
[3] J. F. Kerrisk, “Current distribution and inductance calculation for railgun conductors,” Los Alamos National Laboratory Report no. LA-9092-MS, November 1981.
3
[4] J. F. Kerrisk, “Electrical and Thermal Modelling of Railgun,” IEEE Transactions on Magnetics, vol. 20, no. 2, pp. 399-402, 1984.
4
[5] K.-T. Hsieh, “A Lagrangian formulation for mechanically, thermally coupled electromagnetic diffusive processes with moving conductors,” IEEE Transactions on Magnetics, vol. 31, no. 1, pp. 604–609, 1995.
5
[6] N. Sengil, “Implementation of Monte Carlo Method on Electromagnetic Launcher Simulator,” IEEE Transactions on Plasma Science, vol. 45, no. 5, pp. 1156-1160, 2013.
6
[7] R. Emadifar, S. Tohidi, M. Feyzi, N. Rostami, M. Eldoromi, “Analysis of Magnet Shape Effect on Cogging Torque and EMF Waveform of AFPM Generators Using FEM Methods,” Tabriz Journal of Electrical Engineering, vol. 47, no. 3, pp. 1147-1159, 2017(in persian).
7
[8] A. Darabi, A. Behniafar, H. Tahanian, H. Yoosefi, “Finite Element Modelling of an Inversed Design Circumferential Flux Cylindrical Hysteresis Motor in Steady State Condition,” Tabriz Journal of Electrical Engineering, vol. 47, no. 3, pp. 1001-1012, 2017(in persian).
8
[9] A. Musolino, “Finite-Element Method/Method of Moments Formulation for the Analysis of Current Distribution in Rail Launchers,” IEEE Transactions on Magnetics, vol. 41, no. 1, pp. 387-392, Jan 2005.
9
[10] B. Kim, Kuo-Ta Hsieh, “Effect of Rail/Armature Geometry on Current Density Distribution and inductance gradient,” IEEE Transactions on Magnetics, vol. 35, no.1, pp. 413-416 January 1999.
10
[11] A. Keshtkar, “Effect of rail Dimension on Current Distribution and Inductance Gradient,” IEEE Transactions on Magnetics, vol. 41, no. 1, pp. 383-386, Jan 2005.
11
[12] M. S. Bayati and A. Keshtkar, “Novel Study of the Rails Geometry in the Electromagnetic Launcher,” IEEE Transactions on Plasma Science, vol. 43, no. 5, pp. 1652-1656, 2015.
12
[13] Richard A. Marshall, “Railgun Bore Geometry Round or Square?” IEEE Transactions on Magnetics, vol. 35, no.1, pp. 427-431, 1999.
13
[14] Y. Zhang, J. Ruan, J. Liao, Y. Wang, Y. Zhang and T. Huang, “Salvo Performance Analysis of Triple-Projectile Railgun,” IEEE Transactions on Plasma Science, vol. 41, no. 5, pp. 1421-1425, 2013.
14
[15] Y. Zhang, J. Ruan, J. Liao, Y. Wang, Y. Zhang and T. Huang, “Comparison of Salvo Performance Between Stacked and Paralleled Double-Projectile Railguns,” IEEE Transactions on Plasma Science, vol. 41, no. 5, pp. 1410-1415, 2013.
15
[16] J. C. Schaaf Jr, N. F. Audeh “Solid Armature Coaxial Railgun Experiment Results,” IEEE Transactions on Magnetics, vol. 25, no.1, pp. 711-715, 1993.
16
[17] J. C. Schaaf Jr, N. F. Audeh “Electromagnetic Coaxial Railgun,” IEEE Transactions on Magnetics, vol. 25, no.5, pp. 3263-3265, 1989.
17
ORIGINAL_ARTICLE
T2AS: Topology/Traffic Aware Scheduling to Optimize the End-to-end Delay in IEEE802.154e-TSCH Networks
The Time Synchronized Channel Hopping (TSCH) mode of IEEE 802.15.4e has been widely used as an access method for the industrial Internet of Things (IoT). It permits to overcome the performance limits of 802.15.4 standard in such networks. It provides bounded latency and increased network capacity while mitigating the effects of interference and multipath fading. In this paper, we tackle two critical concerns of industrial networks, namely end-to-end reliability and delay by proposing two centralized scheduling mechanisms; First, the Height-based Scheduling (HS) that computes the schedule only based on the network topology. Second, T2AS, which takes into account both traffic demand and network topology to calculate the schedule. The later mechanism uses a composite weighting function that allows scheduling links with more load and longer distance from the root in earlier timeslots. This prioritizes the flows with more traffic to be scheduled earlier. Both algorithms provide subsequential scheduling for multi-hop scenarios. Simulation results, obtained from the OpenWSN emulator, particularly confirm the efficiency of T2AS in terms of reliability and end-to-end latency. More precisely, it guarantees a reliability of more than 99% for all network sizes. Furthermore, T2AS provides a noticeable bounded delay by delivering data packets within a single slotframe.
https://tjee.tabrizu.ac.ir/article_13330_1a3d53889d53934aa8f49bb94fd7e36a.pdf
2021-04-21
129
137
Link scheduling
802.15.4e-TSCH
slotframe
timeslot
cell
OpenWSN
E.
Mozaffari Ahrar
e.mozaffari@alumni.basu.ac.ir
1
Computer Department, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran.
AUTHOR
M.
Nassiri
m.nassiri@basu.ac.ir
2
Computer Department, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran.
LEAD_AUTHOR
[1] IEEE, “IEEE standard for local and metropolitan area networks–part 15.4:Low-rate wireless personal area networks (lr-wpans)”, IEEE Std802.15.4-2011 (Revision of IEEE Std 802.15.4-2006), Sept 2011, pp. 1–314.
1
[2] F.C. Jiang, H.W. Wu, C. T. Yang, “Trafficload analysis and its application to enhancing longevity on ieee802.15. 4/zigbee sensor network”, The Journal of Supercomputing, vol. 62, no. 2, pp. 895–915, 2012.
2
[3] M. Nassiri, M. Boujari, S. V. Azhari. “Energy-aware and load-balanced parent selection in rplrouting for wireless sensor networks”, International Journal of Wireless and Mobile Computing, vol. 9, no. 3, pp. 231–239, 2015.
3
[4] IEEE, “IEEE Std 802.15.4e, Part. 15.4: Low-Rate Wireless Personal AreaNetworks (LR-WPANs) Amendament 1: MAC sublayer”, IEEE Computer Society, 2012.
4
[5] M. R. Palattella, N. Accettura, M. Dohler, L.A. Grieco, G. Boggia, “Traffic aware scheduling algorithm for reliable low-power multi-hop ieee 802.15. 4e networks”, In 23rd IEEE International Symposium on PersonalIndoor and Mobile Radio Communications (PIMRC), September 2012, Sydney, Australia, pp. 327–332.
5
[6] T. Watteyne, X. Vilajosana, B. Kerkez, F. Chraim, K. Weekly, Q. Wang, S. Glaser, K. Pister, “Openwsn: a standards-based low-power wireless development en-vironment”. Transactions on Emerging Telecommunications Tech-nologies, vol. 23, no. 5, pp. 480–493, 2012.
6
[7] P. Zand, A. Dilo, P. Havinga, “D-MSR: A distributednetwork management scheme for real-time monitoring and pro-cess control applications in wireless industrial automation”, Sensors, vol. 13, no. 7, pp. 8239–8284, 2013.
7
[8] Z. Shelby, K. Hartke, C. Bormann, “The con-strained application protocol (CoAP)”, RFC 7252, June 2014.
8
[9] T. Winter, “Rpl: Ipv6 routing protocol for low-power and lossynetworks”, RFC 6550, March 2012.
9
[10] Y. Jin, P. Kulkarni, J. Wilcox, M. Sooriya-bandara, “A centralized scheduling algorithm for ieee 802.15.4e tsch based industrial low power wireless networks”. In IEEE Wireless Communications and Networking Conference (WCNC), April 2016, Doha, Qatar, pp. 1–6.
10
[11] R. Soua, P. Minet, E. Livolant, “MODESA: Anoptimized multichannel slot assignment for raw data convergecastin wireless sensor networks”, In Proceedings of IEEE 31st International Performance Computing and Communications Conference(IPCCC), December 2012, Austin, TX, USA, pp. 91–100.
11
[12] F. Dobslaw, T. Zhang, M. Gidlund, “End-to-EndReliability-aware Scheduling for Wireless Sensor Networks”, IEEE Transactions on Industrial Informatics, vol. 12, no. 2, pp. 758–767, 2016.
12
[13] T. Huynh, F. Theoleyre, W.J. Hwang, “On the interest of opportunistic anycast scheduling for wireless lowpower lossy networks”, Computer Communications, vol. 104, pp. 55–66, 2017.
13
[14] D. Zorbas, V. Kotsiou, F. Théoleyre, G. ZPapadopoulos, C. Douligeris, “Lost: Localized blacklist-ing aware scheduling algorithm for ieee 802.15. 4-tsch networks”, In Wireless Days (WD), April 2018, Dubai, United Arab Emirates, pp. 110–115.
14
[15] E. M. Ahrar, M. Nassiri, F. Theoleyre, “Multipath aware scheduling for high reliability and fault tolerance in low power industrial networks”, Journal of Network and Computer Applications, vol. 142, pp. 25–36, 2019.
15
[16] K. Brun-Laguna, P. Minet, Y. Tanaka, “Optimized scheduling for time-critical industrial IoT”, In IEEE Global Communications Conference, December 2019, Hawaii, USA, pp. 1-6.
16
[17] S. Jeong, H.S. Kim, J. Paek, S.W. Bahk, “OST: On-demand tsch scheduling with traffic-awareness”, In IEEE Conference on Computer Communications (INFOCOM), July 2020, Virtual Conference.
17
[18] R. Soua, P. Minet, E. Livolant, “DiSCA: A distributed scheduling for convergecast in multichannel wireless sensor networks”, In Proceedings of International Symposium on Integrated Network Management, May 2015, Ottawa, ON, Canada, pp. 156–164.
18
[19] M. Domingo-Prieto, T. Chang, X. Vilajosana, T. Watteyne, “Distributed pid-based scheduling for 6tischnetworks”. IEEE Communications Letters, vol. 20, no. 5, pp. 1006–1009, 2016.
19
[20] M. Sabzevari, M. Nassiri, “A distributed mechanism for cell scheduling to reduce funneling effect in 802.15.4e-based wireless networks”, Tabriz Journal of Electrical Engineering, vol. 46, no. 3, pp. 221–232, 2016 (in persian).
20
[21] D. Dujovne, L. Grieco, M. Palattella, N. Accettura, “6tisch ex-perimental scheduling function (sfx)”, Draft, IETF, March 2018.
21
[22] M. R. Palattella, T. Watteyne, Q. Wang, K. Mu-raoka, N. Accettura, D. Dujovne, L. A. Grieco, T. Engel, “On-the-Fly Bandwidth Reservation for 6TiSCH Wireless Industrial Networks”, IEEE Sensors Journal, vol. 16, pp. 550–560, 2016.
22
[23] M. Zou, J.L. Lu, F. Yang, M. Malaspina, F. Theoleyre, M.Y. Wu, “Distributed scheduling of enhanced beacons for ieee802.15.4-tsch body area networks”. In International Conference on Ad-Hoc Networks and Wireless (ADHOC NOW), July 2016, Lile, France, pp. 3–16.
23
[24] D. De Guglielmo, S. Brienza, G. Anas-Tasi. “A model-based beacon scheduling algorithm for ieee 802.15.4e tsch networks”, In 17th IEEE International Symposium on World of Wireless, Mobile and Multimedia Networks (WoWMoM), June 2016, Coimbra, Portuga, pp. 1–9.
24
[25] H. Bakhshi, S. H. Keshmirifar, “Lifetime improvement and cover-age maximization of cluster-based wireless sensor network usingmulti hop routing”, Tabriz Journal of Electrical Engineering, vol. 47, no. 4, pp. 1637–1647, 2018(in persian).
25
[26] X. Vilajosana, Q. Wang, F. Chraim, T. Watteyne,T. Chang, K. Pister, “A realistic energy consumption model for tsch networks”, IEEE Sensors Journal, vol. 14, no. 2, pp. 482
26
ORIGINAL_ARTICLE
Dynamic output feedback fault-tolerant controller design for a class of generalized Takagi-Sugeno fuzzy nonlinear systems
A novel design approach to construct a fault-tolerant control (FTC) system for a class of nonlinear systems based on a generalized Takagi-Sugeno (GT-S) fuzzy model is proposed. The local rules of the GT-S fuzzy model consist of some multiplicative nonlinear terms. The nonlinear system is affected by actuator faults and unknown disturbances. A state/fault observer is designed and then, a dynamic output feedback scheme is proposed based on the estimated fault and state information. The sufficient conditions for observer and controller design are separately given in terms of linear matrix inequalities (LMIs). It can be shown that the number of LMIs and the computational burden is less than that of similar methods and the effectiveness of the proposed dynamic output feedback FTC approach is verified by proposing simulation results applied to an inverted pendulum system.
https://tjee.tabrizu.ac.ir/article_13299_3082b24069ec0b88f6b8b492f06c2639.pdf
2021-04-21
139
148
nonlinear systems
Generalized Takagi-Sugeno fuzzy model
Fault-tolerant control
Dynamic output feedback
A.
Navarbaf
a_navarbaf@sut.ac.ir
1
Department of Electrical Engineering, Sahand University of Technology, Sahand, Tabriz, Iran.
AUTHOR
M. J.
Khosrowjerdi
khosrowjerdi@sut.ac.ir
2
Department of Electrical Engineering, Sahand University of Technology, Sahand, Tabriz, Iran.
LEAD_AUTHOR
[1] Y. Zhang, J. Jiang, “Bibliographical review on reconfigurable fault-tolerant control systems”, Annual Reviews in Control, vol. 32, no. 2, pp. 229-252, 2008.
1
[2] P. M. Frank, “Analytical and qualitative model-based fault diagnosis–a survey and some new results”, European Journal of control, vol. 2, no. 1, pp. 6-28, 1996.
2
[3] S. X. Ding, “Model-based fault diagnosis techniques: design schemes, algorithms, and tools”, Springer Science & Business Media, 2008.
3
[4] M. Witczak, “Fault diagnosis and fault-tolerant control strategies for non-linear systems: Analytical and soft computing approaches”, Springer International Publishing, 2014.
4
[5] J. Gertler, “Fault detection and diagnosis in engineering systems”, Routledge, 2017.
5
[6] R. Isermann, “Supervision, fault-detection and fault-diagnosis methods—a short introduction”, In Combustion Engine Diagnosis, pp. 25-47, Springer Vieweg, Berlin, Heidelberg, 2017.
6
[7] Z. Gao, C. Cecati, S. X. Ding, “A survey of fault diagnosis and fault-tolerant techniques—Part I: Fault diagnosis with model-based and signal-based approaches”, IEEE Transactions on Industrial Electronics, vol. 62, no. 6, pp. 3757-3767, 2015.
7
[8] Z. Gao, C. Cecati, S. X. Ding, “A survey of fault diagnosis and fault-tolerant techniques—Part II: Fault diagnosis with knowledge-based and hybrid/active approaches”, IEEE Transactions on Industrial Electronics, vol. 62, no. 6, pp. 3768-3774, 2015.
8
[9] H. Kargar, J. Zarei, R. Razavi-Far, “Robust fault detection filter design for nonlinear networked control systems with time-varying delays and packet dropout”, Circuits, Systems, and Signal Processing, vol. 38, no. 1, pp. 63-84, 2019.
9
[10] M. Li, Z. Zuo, H. Liu, C. Liu, B. Zhu, “Adaptive fault tolerant control for trajectory tracking of a quadrotor helicopter”, Transactions of the Institute of Measurement and Control, vol. 40, no. 12, pp. 3560-3569, 2018.
10
[11] M. Witczak, V. Puig, S. M. de Oca, “A fault-tolerant control strategy for non-linear discrete-time systems: application to the twin-rotor system”, International Journal of Control, vol. 86, no. 10, pp. 1788-1799, 2013.
11
[12] X. Jin, “Adaptive fault tolerant tracking control for a class of stochastic nonlinear systems with output constraint and actuator faults”, Systems & Control Letters, vol. 107, pp. 100-109, 2017.
12
[13] X. Yin, Z. Li, L. Zhang, M. Han, “Distributed state estimation of sensor-network systems subject to Markovian channel switching with application to a chemical process”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 6, pp. 864-874, 2018.
13
[14] L. Liu, Y. J. Liu, S. Tong, “Neural networks-based adaptive finite-time fault-tolerant control for a class of strict-feedback switched nonlinear systems”, IEEE Transactions on Cybernetics, vol. 49, no. 7, pp. 2536-2545, 2018.
14
[15] X. Yun, L. Wu, Y. Xu, “Adaptive fault-tolerant control for a class of uncertain lower-triangular nonlinear systems with actuator failures”, In 2018 Chinese Control and Decision Conference (CCDC), June 2018, Shenyang, China, pp. 795-800.
15
[16] A. Moradvandi, S. A. Malek, M. Shahrokhi, “Adaptive finite-time fault-tolerant controller for a class of uncertain MIMO nonlinear switched systems subject to output constraints and unknown input nonlinearities”, Nonlinear Analysis: Hybrid Systems, vol. 35, February 2020. DOI: 10.1016/j.nahs.2019.100821
16
[17] X. Jin, X. Zhao, J. Yu, X. Wu, J. Chi, “Adaptive fault-tolerant consensus for a class of leader-following systems using neural network learning strategy”, Neural Networks, vol. 121, pp. 474-483, 2020.
17
[18] A. Khodadadi, M. Shahriari-kahkeshi, A. Chatraei, “A novel scheme for actuator fault tolerant controller design based on the fault identification”, Tabriz Journal of Electrical Engineering, vol. 48, no. 2, pp. 595-608, 2018 (in persian).
18
[19] J. L. Castro, M. Delgado, “Fuzzy systems with defuzzification are universal approximators”, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 26, no. 1, pp. 149-152, 1996.
19
[20] C. W. De Silva, “Intelligent control: fuzzy logic applications”, CRC press, 2018.
20
[21] K. Tanaka, H. O. Wang, “Fuzzy control systems design and analysis: A linear matrix inequality approach”, John Wiley & Sons, 2004.
21
[22] J. Mrazgua, M. Ouahi, “Fuzzy fault-tolerant H∞ control approach for nonlinear active suspension systems with actuator failure”, Procedia Computer Science, vol. 148, pp. 465-474, 2019.
22
[23] X. K. Du, H. Zhao, X. H. Chang, “Unknown input observer design for fuzzy systems with uncertainties”, Applied Mathematics and Computation, vol. 266, pp. 108-118, 2015.
23
[24] J. Dong, G. H. Yang, “Observer-based output feedback control for discrete-time T-S fuzzy systems with partly immeasurable premise variables”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no.(1), pp. 98-110, 2017.
24
[25] D. Ye, X. Li, “Event-triggered fault detection for continuous-time networked polynomial-fuzzy-model-based systems”, Applied Mathematics and Computation, vol. 366, February 2020. DOI: 10.1016/j.amc.2019.124729
25
[26] Y. Gao, F. Xiao, J. Liu, R. Wang, “Distributed soft fault detection for interval type-2 fuzzy-model-based stochastic systems with wireless sensor networks”, IEEE Transactions on Industrial Informatics, vol. 15, no. 1, pp. 334-347, 2018.
26
[27] S. Zeghlache, A. Djerioui, L. Benyettou, T. Benslimane, H. Mekki, A. Bouguerra, “Fault tolerant control for modified quadrotor via adaptive type-2 fuzzy backstepping subject to actuator faults”, ISA transactions, vol. 95, pp. 330-345, 2019.
27
[28] H. Patel and V. Shah, “Fault tolerant control using interval type-2 Takagi-Sugeno fuzzy controller for nonlinear system”, In International Conference on Intelligent Systems Design and Applications, December 2018, Vellore, India, pp. 150-164.
28
[29] J. Tan, S. Dian, T. Zhao, “Further studies on stability and stabilization of T-S fuzzy systems with time-varying delays via fuzzy Lyapunov-Krasovskii functional method”, Asian Journal of Control, vol. 20, no. 6, pp. 2207-2222, 2018.
29
[30] D. Kharrat, H. Gassara, A. El Hajjaji, M. Chaabane, “Adaptive fuzzy observer-based fault-tolerant control for Takagi-Sugeno descriptor nonlinear systems with time delay”, Circuits, Systems, and Signal Processing, vol. 37, no. 4, pp. 1542-1561, 2018.
30
[31] S. Asadi, A. Khayatian, M. Dehghani, N. Vafamand, “Robust TS fuzzy-based sliding mode observer design for actuator fault reconstruction: Non-quadratic Lyapunov function approach”, Tabriz Journal of Electrical Engineering, vol. 49, no. 1, pp. 25-35, 2019 (in persian).
31
[32] R. Rajesh, M. R. Kaimal, “T-S fuzzy model with nonlinear consequence and PDC controller for a class of nonlinear control systems”, Applied Soft Computing, vol. 7, no. 3, pp. 772-782, 2007.
32
[33] J. Dong, Y. Wang, G. H. Yang, “Control synthesis of continuous-time TS fuzzy systems with local nonlinear models”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 39, no. 5, pp. 1245-1258, 2009.
33
[34] J. Dong, Y. Wang, G. H. Yang, “Output feedback fuzzy controller design with local nonlinear feedback laws for discrete-time nonlinear systems”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 40, no. 6, pp. 1447-1459, 2010.
34
[35] J. Dong, Y. Wang, G. H. Yang, “H∞ and mixed H2/ H∞ control of discrete-time T-S fuzzy systems with local nonlinear models”, Fuzzy Sets and Systems, vol. 164, no. 1, pp. 1-24, 2011.
35
[36] H. Moodi, M. Farrokhi, “Robust observer-based controller design for Takagi-Sugeno systems with nonlinear consequent parts”, Fuzzy Sets and Systems, vol. 273, pp. 141-154, 2015.
36
[37] H. Moodi, A. Kazemy, “Robust controller design for Takagi-Sugeno systems with nonlinear consequent part and time delay”, International Journal of Fuzzy Systems, vol. 21, no. 3, pp. 745-754, 2019.
37
[38] H. Moodi, D. Bustan, “Wind turbine control using TS systems with nonlinear consequent parts”, Energy, vol. 172, pp. 922-931, 2019.
38
[39] H. Wang, D. Ye, G. H. Yang, “Actuator fault diagnosis for uncertain T-S fuzzy systems with local nonlinear models”, Nonlinear Dynamics, vol. 76, no. 4, pp. 1977-1988, 2014.
39
[40] J. Han, H. Zhang, Y. Wang, Y. Liu, “Disturbance observer based fault estimation and dynamic output feedback fault tolerant control for fuzzy systems with local nonlinear models”, ISA transactions, vol. 59, pp. 114-124, 2015.
40
[41] J. Han, H. Zhang, Y. Wang, X. Liu, “Robust fault estimation and accommodation for a class of T-S fuzzy systems with local nonlinear models”, Circuits, Systems, and Signal Processing, vol. 35, no. 10, pp. 3506-3530, 2016.
41
[42] M. Klug, E. B. Castelan, V. J. Leite, L. F. Silva, “Fuzzy dynamic output feedback control through nonlinear Takagi-Sugeno models”, Fuzzy Sets and Systems, vol. 263, pp. 92-111, 2015.
42
[43] S. Ochiai, J. Yoneyama, and Y. Uchida, “Guaranteed cost control design based on Takagi-Sugeno fuzzy systems with nonlinear subsystems”, In 2013 IEEE International Conference on Systems, Man, and Cybernetics, October 2013, Manchester, UK, pp. 4712-4717.
43
[44] J. Yoneyama, “Output feedback control design for nonlinear systems based on a generalized Takagi-Sugeno fuzzy system”, In 2014 World Automation Congress (WAC), August 2014, Waikoloa, HI, USA, pp. 313-318.
44
[45] J. Yoneyama, “Nonlinear control design based on generalized Takagi-Sugeno fuzzy systems”, Journal of the Franklin Institute, vol. 351, no. 7, pp. 3524-3535, 2014.
45
[46] K. Zhang, B. Jiang, M. Staroswiecki, “Dynamic output feedback-fault tolerant controller design for Takagi-Sugeno fuzzy systems with actuator faults”, IEEE Transactions on Fuzzy Systems, vol. 18, no. 1, pp. 194-201, 2010.
46
[47] J. Dong, J. Hou, “Output feedback fault-tolerant control by a set-theoretic description of T-S fuzzy systems”, Applied Mathematics and Computation, vol. 301, pp. 117-134, 2017.
47
[48] A. Navarbaf, M. J. Khosrowjerdi, “Fault-tolerant controller design with fault estimation capability for a class of nonlinear systems using generalized Takagi-Sugeno fuzzy model”, Transactions of the Institute of Measurement and Control, vol. 41, no. 15, pp. 4218-4229, 2019.
48
[49] H. D. Tuan, P. Apkarian, T. Narikiyo, Y. Yamamoto, “Parameterized linear matrix inequality techniques in fuzzy control system design”, IEEE Transactions on fuzzy systems, vol. 9, no. 2, pp. 324-332, 2001.
49
[50] Y. Wang, L. Xie, C. E. de Souza, “Robust control of a class of uncertain nonlinear systems”, Systems & Control Letters, vol. 19, no. 2, pp. 139-149, 1992.
50
[51] P. Gahinet, A. Nemirovskii, A. J. Laub, M. Chilali, “The LMI control toolbox”, In Proceedings of the 33rd IEEE Conference on Decision and Control, December 1994, Lake Buena Vista, FL, USA, pp. 2038-2041.
51
[53] J. Lofberg, “YALMIP: a toolbox for modeling and optimization in MATLAB”, In 2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No.04CH37508), September 2004, New Orleans, LA, USA, pp. 284-289.
52