Using Fuzzy Controllability Property in Robust Controller Designing for a Class of T-S Fuzzy Model

Authors

1 Electrical Engineering Faculty, Najafabad Branch, Islamic Azad University, Najafabad, Iran

2 Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran

Abstract

Sincemost of the systems in real world are nonlinear and include uncertainty in their nature, robust controller designing is one of the most important challenges for engineers. Controller designing for such systems is usually complicate with high computational cost. In contrast to this, state feedback controller designing, based on well-known Ackermann’s formula, has simplicity in designing and application although global states controllability should be considered seriously. The aim of this paper is to design a state feedback controller for nonlinear inverted pendulum with uncertainty which close loop system has global asymptotically stability. For this reason, controllability property for nonlinear systems has been analyzed based on TS- Fuzzy model. In the existence of uncertainty, controllability property might be failed. In this case to handle the uncertainties in the systems, sufficient conditions have been investigated to guarantee the local and global controllability conditions and also global stability conditions. The advantage of this method is simplicity in implementation comparing to other complicated controllers.

Keywords


[1] W. Xie, “Improved L2 gain performance controller synthesis for Takagi– Sugeno fuzzy system,” IEEE Trans. Fuzzy Syst., vol. 16, no. 5, pp. 1142– 1150, Oct. 2008.
[2] W. H. Ho, J. T. Tsai and J. H. Chou, “Robust quadratic-optimal control of TS-fuzzy-model-based dynamic systems with both elemental parametric uncertainties and norm-bounded approximation error,” IEEETrans. FuzzySyst., vol. 17, no. 3,
[3] F. Wang, Z. R. Feng, S. Liu and P. Jiang, “Robust supervisory control of fuzzy discrete event systems,” IET Control Theory Appl., vol. 2, no. 5, pp. 384–391, May 2008.
[4] W. Yang, “Supervisory control theory of fuzzy discrete event systems,”Acta Autom. Sin., vol. 34, no. 4, pp. 460–465, Aug. 2008.
[5] A. S. M. Biglarbegian and W. Melek, “On the accessibility/controllability of fuzzy control systems,” Inf. Sci, vol. 202, pp. 58-72, 2012.
[6] A. K. Yadav, P. Gaur, A. P. Mittal and M. Anzar, “Comparative analysis of various control techniques for inverted pendulum,” India International Conference on Power Electronics (IICPE2010). IEEE, pp.1-6, 2011.‌
[7] I. Abdelmalek, N. Golea and M. Laid Hadjili, “A new fuzzy Lyapunov approach to non-quadratic stabilization of Takagi–Sugeno fuzzy models,” Int. J. Appl. Math. Comput. Sci., vol. 17, no. 1, pp. 39–51, 2007.
[8] A. Raees, M.B. Kadri, “Fuzzy Model based Predictive control of a Hammerstein model with constraints handling,” In: Satellite Telecommunications (ESTEL), IEEE First AESS European Conference on. IEEE, pp. 1-6, 2012.
[9] R. Pradhan and S. Panda, “Application of Genetic Algorithm based PSS for two-area AGC system in deregulated scenario,” In: Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on. IEEE, pp. 1207-1212, 2009.
[10] M. Campana and J. Alberto, “Analysis of the Fuzzy Controllability Property and Stabilization for a Class of T–S Fuzzy Models,” Fuzzy Systems, IEEE Transactions, vol. 23, no. 2, pp. 291-301, 2015.
[11] S. Chen, W. Ho and J. Chou, “Robust controllability of T–S fuzzy-model-based control systems with parametric uncertainties,” IEEE Transactions on Fuzzy Systems vol. 17, no. 6, pp. 1324-1335, 2009.
[12] A. Sinha, Linear Systems: Optimal and Robust Control, London, U.K.: CRC press, 2007.
[13] H. H. Rosenbrock, State-Space and Multivariable Theory, New York: Wiley, 1970.
[14] H. Li, X. Sun, L. Wu, “State and output feedback control of a class of fuzzy systems with mismatched membership functions,” IEEE Transactions on Fuzzy Systems, vol. 23, no. 6, pp. 1943 - 1957, 2015.