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This paper introduces a brushless drive system with an adaptive fuzzy-neural-network controller. First, a neural network-based architecture is described for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the neural network structure. Then, the fuzzy rules and input-output of the system are tuned by the supervised gradient decent learning algorithm. Using an experimental setup, the performance of the proposed controller is evaluated under various operating conditions. Test results are presented and discussed. The controller is shown to be robust, adaptive, and capable of learning. The effectiveness of the fuzzy-neural-network controller is demonstrated by its encouraging study results, when compared with those of a proportional-integral controller.
Rubaai et al. (Tue,) studied this question.