Optimal fuzzy tracking control of uncertain nonlinear systems based on genetic algorithms and fuzzy Lyapunov function

In this paper, we propose a practical robust fuzzy control design scheme that achieves optimal tracking performance and requires limited accuracy in the plant model. The plant is identified as a fuzzy combination of Takagi-Sugeno type linear models, and the fuzzy controller is optimized by genetic algorithms according to both the tracking performance and the attenuation level. The procedure applies the lately proposed idea of the fuzzy Lyapunov function that is less conservative then the traditional Lyapunov function candidate approach, and ensures H_∞ robust tracking. The effectiveness of the proposed scheme is demonstrated by the fuzzy tracking control of an uncertain chaotic system with external disturbance.