Development and implementation of an adaptive fuzzy-neural-network controller for brushless drives

A brushless DC motor drive with a proposed adaptive fuzzy-neural-network controller is introduced in this paper. First, a neural network-based architecture is introduced 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 descent learning algorithm. Using an experimental setup, the performance of the proposed controller is evaluated under various operating conditions. Test results are presented and discussed in the paper. The presented controller is shown to be robust, adaptive and capable of learning. To demonstrate the effectiveness of the controller, a proportional-integral controller has been used to perform comparative studies with encouraging results.