کنترل ردیاب کوادروتور با استفاده از کنترل مد لغزشی تطبیقی مبتنی‌بر شبکه‌های عصبی چبیشف

نوع مقاله : علمی-پژوهشی

نویسندگان

دانشکده مهندسی برق، واحد نجف آباد، دانشگاه آزاد اسلامی

چکیده

در این مقاله، روشی برای کنترل کوادروتور براساس کنترل مد لغزشی با استفاده از شبکه‌های عصبی چبیشف پیشنهاد شده است. روش پیشنهادی ترکیبی از روش کنترل مدلغزشی و تخمینگر شبکه عصبی چبیشف می‌باشد که در آن وزن‌های شبکه عصبی به‌صورت بلادرنگ با استفاده از تکنیک‌های کنترل تطبیقی مقاوم به‌روزرسانی می‌شوند. در این پژوهش، مدل دینامیکی کوادروتور به‌منظور کنترل ردیابی وضعیت و زاویه کوادروتور به دو زیرسیستم تقسیم شده است: یک زیرسیستم تحریک کامل و دیگری تحریک نقصانی. برای زیرسیستم اول، سطوح لغزش تنها با استفاده از خطای ردیابی یکی از متغیرهای حالت طراحی می‌شوند و برای زیرسیستم دوم، سطوح لغزش با ترکیب خطی از دو متغیر حالت تعریف می‌شوند. در این مقاله، پایداری سیستم به‌وسیله تکنیک‌های مبتنی بر تئوری لیاپانوف تحلیل می‌گردد و با استفاده از نتایج شبیه‌سازی صحت عملکرد کنترل‌کننده نشان داده خواهد شد.

کلیدواژه‌ها


عنوان مقاله [English]

Tracking Control of Quadrotor by using Adaptive Sliding-Mode Control based on Chebyshev Neural Networks

نویسندگان [English]

  • M. Khashei Varnamkhasti
  • Kh. Shojaei Arani
Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
چکیده [English]

In this paper, a method is proposed for the control of a quadrotor based on sliding mode control by using Chebyshev neural networks. The proposed approach is a combination of the sliding mode controller and the Chebyshev neural network approximator that the neural network weights are tuned in real-time by using robust adaptive techniques. In this research, the dynamic model of the quadrotor is divided into two subsystems for the purpose of the position and orientation tracking control: a fully-actuated subsystem and an underactuated subsystem. For the former, the sliding surfaces are designed by using one state variable, and for the latter, the sliding manifolds are defined by a linear combination of two state variables. In this paper, the system stability is analyzed by Lyapunov theory-based techniques and the accuracy of the controller performance will be illustrated by the simulation results.

کلیدواژه‌ها [English]

  • Quadrotor
  • sliding mode control
  • chebyshev neural networks
  • adaptive control
  • robust control
[1]      K. D. Young, V. I. Utkin and U. Ozguner, “A Control Engineer’s Guide to Sliding Mode Control,” IEEE Transactions on Control Systems Technology, vol. 7, no. 3, pp. 328–342, 1999.
[2]      J-J. E. Slotine and W. Li, Applied Nonlinear Control, New Jersey: Prentice-Hall, 1991.
[3]      W. Perruquetti and J. Pierre-Barbot, Sliding Mode Control in Engineering, New York: Marcel Dekker, 2002.
[4]      W. Gao and J. C. Hung, “Variable Structure Control of Nonlinear Systems: A New Approach,” IEEE Transactions on Industrial Electronics, vol. 40, no. 1, pp. 45–55, 1993.
[5]      E-H. Zheng, J-J. Xiong and J-L. Luo, "Second order sliding mode control for a quadrotor UAV," ISA Transactions, vol. 53, no. 4, pp. 1350–1356, 2016.
[6]      R. Xu and U. Ozguner, "Sliding mode control of a Quadrotor helicopter," in Proceedings of the 45th IEEE Conference on Decision and Control, pp. 4957–4962, Dec. 2006.
[7]      J-J. Xiong and G. Zhang, "Sliding mode control for a quadrotor UAV with parameter uncertainties," 2nd International Conference on Control, Automation and Robotics (ICCAR), pp. 207–212, Apr. 2016.
[8]      Y-H. Pao and Y. Takefuji, "Functional-link net computing: theory, system architecture, and functionalities," Computer, vol. 25, no. 5, pp. 76–79, May 1992.
[9]      J. Liu and X. Wang, "Advanced sliding mode control for mechanical systems: design, analysis and MATLAB simulation," Berlin: Springer, 2014.
[10]      A-M. Zou, K. D. Kumar, and Z.-G. Hou, "Quaternion-Based Adaptive output feedback attitude control of spacecraft using Chebyshev neural networks," IEEE Transactions on Neural Networks, vol. 21, no. 9, pp. 1457–1471, Sep. 2010.
[11]      A.-M. Zou, K. D. Kumar, Z.-G. Hou and X. Liu, "Finite-time attitude tracking control for spacecraft using terminal sliding mode and Chebyshev neural network," IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 41, no. 4, pp. 950–963, Aug. 2011.
[12]      J. C. Patra and A. C. Kot, "Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks," IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, vol. 32, no. 4, pp. 505–511, Aug. 2002.
[13]      S. Purwar, I. N. Kar, and A. N. Jha, "On-line system identification of complex systems using Chebyshev neural networks," Applied Soft Computing, vol. 7, no. 1, pp. 364–372, Jan. 2007.
[14]      B. Y. Vyas, B. Das and R. P. Maheshwari, "Improved fault classification in series compensated transmission line: Comparative evaluation of Chebyshev neural network training Algorithms," IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 8, pp. 1631–1642, Aug. 2016.
[15]      B. Pratap, "Neuro sliding mode controller for twin rotor control system," 2012 Students Conference on Engineering and Systems, India, Mar. 2012.
[16]      F. Abdollahi, H. A. Talebi and R. V. Patel, "A stable neural network-based observer with application to flexible-joint manipulators," IEEE Transactions on Neural Networks, vol. 17, no. 1, pp. 118–129, Jan. 2006.
[17]      R. Xu and Ü. Özgüner, "Sliding mode control of a class of underactuated systems," Automatica, vol. 44, no. 1, pp. 233–241, Jan. 2008. 
[18]      L. Besnard, Y. B. Shtessel and B. Landrum, "Quadrotor vehicle control via sliding mode controller driven by sliding mode disturbance observer," Journal of the Franklin Institute, vol. 349, no. 2, pp. 658–684, Mar. 2012.
[19]      Z. Weidong, Z. Pengxiang, W. Changlong and C. Min, "Position and attitude tracking control for a quadrotor UAV based on terminal sliding mode control," 2015 34th Chinese Control Conference (CCC), pp. 3398–3404, Jul. 2015.
[20]      H. Bouadi, A. Aoudjif and M. Guenifi, "Adaptive flight control for quadrotor UAV in the presence of external disturbances," 2015 6th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO), pp. 1–6, May 2015.
[21]      H. Lee and V. I. Utkin, "Chattering suppression methods in sliding mode control systems," Annual Reviews in Control, vol. 31, no. 2, pp. 179–188, Jan. 2007.
[22]      A. Das, F. Lewis and K. Subbarao, "Backstepping approach for controlling a Quadrotor using Lagrange form dynamics," Journal of Intelligent and Robotic Systems, vol. 56, no. 1-2, pp. 127–151, Apr. 2009.
[23]      C. Nicol, C.J.B. Macnab and A. Ramirez-Serrano, “Robust Neural Network control of a Quadrotor Helicopter,” IEEE Fuzzy information PSC, pp. 454-458, 2008.
[24]      B.-Y. Lee, H.-I. Lee and M.-J. Tahk, "Analysis of adaptive control using on-line neural networks for a quadrotor UAV," 2013 13th International Conference on Control, Automation and Systems (ICCAS 2013), pp. 1840–1844, Oct. 2013.
[25]      J. O. Pedro and A. J. Crouse, "Direct adaptive neural control of a quadrotor unmanned aerial vehicle," 10th Asian Control Conference (ASCC), pp. 1–6, May 2015.
[26]      Q. Lin, Z. Cai, Y. Wang, J. Yang and L. Chen, "Adaptive flight control design for Quadrotor UAV based on dynamic inversion and neural networks," 2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control, pp. 1461-1466, Sep. 2013.
[27]      T. Dierks and S. Jagannathan, "Output feedback control of a Quadrotor UAV using neural networks," IEEE Transactions on Neural Networks, vol. 21, no. 1, pp. 50–66, Jan. 2010.
[28]      T.-T. Lee and J.-T. Jeng, "The Chebyshev-polynomials-based unified model neural networks for function approximation," IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, vol. 28, no. 6, pp. 925–935, 1998.
[29]      G. Sun, X. Ren and D. Li, "Neural active disturbance rejection output control of Multimotor Servomechanism," IEEE Transactions on Control Systems Technology, vol. 23, no. 2, pp. 746–753, Mar. 2015.
[30]      V. Sharma and S. Purwar, "Nonlinear controllers for a light-weighted all-electric vehicle using Chebyshev neural network," International Journal of Vehicular Technology, vol. 2014, pp. 1–14, 2014.
[31]      A.-M. Zou and K. D. Kumar, "Adaptive attitude control of spacecraft without velocity measurements using Chebyshev neural network," Acta Astronautica, vol. 66, no. 5-6, pp. 769–779, Mar. 2010.
[32]      H. K. Khalil, Nonlinear systems, 3rd Ed., New Jersey: Prentice-Hall, 2002.
[33]      M. M. Polycarpou, "Stable Adaptive Neural Control Scheme for Nonlinear Systems," IEEE Transactions on Automatic Control, vol. 41, no .3, pp. 447-451, 1996.
[34]      S. Yu, X. Yu, B. Shirinzadeh and Z. Man, “Continuous finite-time control for robotic manipulators with terminal sliding mode,” Automatica, vol. 41, no. 11, pp. 1957–1964, Nov. 2005.
[35]      Y. Jiang, Q. Wang and C. Dong, "A reaching law based neural network terminal sliding-mode guidance law design," 2013 IEEE International Conference of IEEE Region 10 (TENCON 2013), Xian, China, 2013.
[36]      Omid Mofid and Saleh Mobayen.,"Adaptive sliding mode control for finite-time stability of quad-rotor UAVs with parametric uncertainties," ISA Transactions, vol. 72, pp. 1-14, 2018.
[37]      علیرضا مدیرروستا، مهدی خدابنده، "طراحی یک روش کنترل مد لغزشی انتگرالی تطبیقی برای پایدارسازی زمان محدود و مقاوم پرنده چهارملخه،" مجله مهندسی برق دانشگاه تبریز، جلد 46، شماره 1، صفحات 321-332، 1395.
[38]      محسن وحدانی‌پور، مهدی خدابنده، "کنترل مد لغزشی مبتنی بر روش برگشت به عقب کوادروتور با حذف اثر اغتشاش بار و تخمین اینرسی به روش تطبیقی،" مجله مهندسی برق دانشگاه تبریز، جلد 47، شماره 2، صفحات 775-783، 1396.