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By G. Russell, I. Sayers

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However, most real physical systems are nonlinear in nature and usually subject to uncertainties. In this case, the linear dynamic systems are not powerful to describe these practical systems. So, it is important to design robust control of nonlinear models. ). These two last decades, fuzzy model control has been extensively studied; see (Zhang & Heng, 2002)-(Chadli & ElHajjaji, 2006)-(Kim & Lee, 2000)-(Boukas & ElHajjaji, 2006) and the references therein because T-S fuzzy model can provide an effective representation of complex nonlinear systems.

Moreover, by using the H∞ approach, we ensure that the estimation error converges faster to the equilibrium point zero. This chapter is organized as follows: in Section 2, we give the class of uncertain fuzzy models, the observer-based fuzzy controller structure and the control objectives. After reviewing existing LMI constraints for a pole placement in Section 3, we propose the new conditions for the uncertain augmented T-S fuzzy system containing both the fuzzy controller as well as the observer dynamics.

Liao, X. (2004). Delay-dependent robust stability of uncertain fuzzy systems with time-varying delays, Control Theory and Applications, IEE Proceedings, IET, pp. 417–421. , Wang, Q. & Lee, T. (2006). Delay-dependent lmi conditions for stability and stabilization of t-s fuzzy systems with bounded time-delay, Fuzzy sets and systems Vol. 157(No. 9): 1229 – 1247. Moon, Y. , Kwon, W. H. & Lee, Y. S. (2001). Delay-dependent robust stabilization of uncertain state-delayed systems, International Journal of control Vol.

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Advanced Simulation and Test Methodols for VLSI Design by G. Russell, I. Sayers


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