Fuzzy Control Schemes via a Fuzzy Performance Evaluator

Part of the Control Engineering book series (CONTRENGIN)


In practical control systems, the plants are always nonlinear and with uncertainty. It is a difficult process to design a stable controller for such nonlinear plants. In the last few years, fuzzy control of nonlinear and uncertain systems has been an exciting research area and some significant results have been achieved in [4,7,10,12, 13, 14, 15, 16, 17]. Fuzzy control can be divided into model-based methods and model-free methods according to whether a fuzzy model is needed. For model-based control methods, the theoretical foundation is the universal approximation theory. Most researchers use fuzzy logic systems as approximators for nonlinear and uncertain systems or controllers, and use the Lyapunov second method to analyze the stability of fuzzy logic systems. Considering the influence of both fuzzy logic system approximation error and external disturbance, fuzzy robust control schemes have been addressed in [4,7]. A fuzzy basis function vector-based adaptive control scheme for control of multipleinput multiple-output. (MIMO) systems with square and nonsquare nonlinearity is proposed in [16,17]. An observer-based adaptive fuzzy-neural control scheme is proposed in [7]. In those studies, the upper bound of external disturbance must be known. The disturbance attenuation term is determined based on the known upper bound which leads to a conservative design scheme.


External Disturbance Fuzzy Model Fuzzy Control Tracking Performance Fuzzy Logic System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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