Self-Learning Knowledge Systems and Fuzzy Systems and Their Applications

Publisher Summary In recent years, fuzzy systems—a branch of artificial intelligence (AI)—have attracted considerable attention as a candidate for novel computational systems because of the variety of advantages that they offer over conventional computational systems. Fuzzy control systems incorporate an alternative approach to the classical design approach, which requires a deep understanding of the system or exact highly complicated nonlinear mathematical models. They have been found to be a good replacement for conventional control techniques. Moreover, rapidity and robustness are the most profound and interesting properties in comparison to the classical scheme. This chapter presents self-learning fuzzy control systems and their application in power systems excitation control. The design steps required for constructing such a system are explained in detail. Without resorting to another controller as a reference, a self-learning fuzzy control system is proposed to construct a fuzzy system that performs a prescribed task. To train the fuzzy control system, the back-propagation method is employed to propagate the plant output error signal through different stages in time. This algorithm is applied to power systems excitation control. Results show that the proposed fuzzy control system can provide good damping of the power system over a wide range and significantly improve the dynamic performance of the system.

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