Universal Fuzzy Controllers For Non Affine Nonlinear Systems
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Universal Fuzzy Controllers for Non-affine Nonlinear Systems
This thesis provides a systematic and integral answer to an open problem concerning the universality of dynamic fuzzy controllers. It presents a number of novel ideas and approaches to various issues including universal function approximation, universal fuzzy models, universal fuzzy stabilization controllers, and universal fuzzy integral sliding mode controllers. The proposed control design criteria can be conveniently verified using the MATLAB toolbox. Moreover, the thesis provides a new, easy-to-use form of fuzzy variable structure control. Emphasis is given to the point that, in the context of deterministic/stochastic systems in general, the authors are in fact discussing non-affine nonlinear systems using a class of generalized T-S fuzzy models, which offer considerable potential in a wide range of applications.
Differential Evolution Algorithm with Type-2 Fuzzy Logic for Dynamic Parameter Adaptation with Application to Intelligent Control
This book focuses on the fields of fuzzy logic, bio-inspired algorithm, especially the differential evolution algorithm and also considering the fuzzy control area. The main idea is that these two areas together can help solve various control problems and to find better results. In this book, the authors test the proposed method using five benchmark control problems. First, the water tank, temperature, mobile robot, and inverted pendulum controllers are considered. For these 4 problems, experimentation was carried out using a Type-1 fuzzy system and an Interval Type-2 system. The last control problem was the D.C. motor, for which the experiments were performed with Type-1, Interval Type-2, and Generalized Type-2 fuzzy systems. When we use fuzzy systems combined with the differential evolution algorithm, we can notice that the results obtained in each of the controllers are better and with increasing uncertainty, the results are even better. For this reason, the authors consider in this book the proposed method using fuzzy systems and the differential evolution algorithm to improve the fuzzy controllers’ behavior in complex control problems.