A major difficulty with fuzzy logic control is the development of an adequate rule-base and its modification such that the desired performance is maintained with changing operating conditions. Rule elicitation is a lengthy process and specific to each application. To overcome this problem, the concepts of self-organizing fuzzy logic control and other self-learning approaches have been widely investigated. In this paper, a different type of learning fuzzy control algorithm has been discussed. The control method has been called Model Reference Fuzzy Adaptive Control (MRFAC) indicating the method of adaptation. A reference model is used to provide performance feedback for automatically synthesizing and modifying a fuzzy controller’s rule-base on-line, as new information on how to control the system is gathered. The method employs a simple iterative adaptation algorithm compared to other methods in the literature. The design issues involving the method have been thoroughly investigated. Extensive simulation tests have been executed on the MRFAC to examine the effectiveness of choice of its different parameters. The results provide an useful insight into the design considerations involving the MRFAC.
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