Intelligent Speed Control of Permanent Magnet Synchronous Motor Drive Based-on Neuro-Fuzzy Approach

This paper presents speed control system for permanent magnet synchronous motor (PMSM) drive, which newly incorporates adaptive neuro-fuzzy with two input variables and one control output variable. This control methodology solves the problem of nonlinearities and parameter variations of PMSM drive. Moreover, it achieves high dynamic performance and accurate speed tracking control with good steady-state characteristics. Firstly, a neural network-based architecture is described for fuzzy logic control. The specified rules and their membership functions of fuzzy systems are represented as the processing nodes in the neural network structure. Then, the fuzzy rules and the membership functions are tuned by the supervised gradient decent learning algorithm. The performance of the proposed controller is evaluated under various operating conditions. The controller is shown to be robust, inherently adaptive in nature, and capable of learning. Results demonstrate the efficacy of the control system

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