標題: Adaptive Neural Predictive Control for Permanent Magnet Synchronous Motor Systems With Long Delay Time
作者: Wu, Bing-Fei
Lin, Chun-Hsien
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: Model predictive control;neural network;non-linear optimization;extended Kalman filter;fuzzy rule
公開日期: 1-Jan-2019
摘要: Since the permanent magnet synchronous motor system in this research needs about 40 ms to finish a control cycle, such a long delay in time strongly causes the bad performance for the conventional controllers, especially for position control. To well control the speed and position, an adaptive neural predictive control is proposed. A two-layer recursive neural network is employed as a speed predictor, and an extended Kalman filter is utilized to tune the parameters of the predictor adaptively. Chaos optimization algorithm and Newton-Raphson optimization are combined to solve the problem of predictive control. As for the speed control, the proposed method shows better performance. The position control is designed based on the speed control. Due to the physical limitation of the plant, the steady state error is still large. Hence, a fuzzy compensator is applied. From the experiment, the error is reduced obviously.
URI: http://dx.doi.org/10.1109/ACCESS.2019.2932746
http://hdl.handle.net/11536/152806
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2932746
期刊: IEEE ACCESS
Volume: 7
起始頁: 108061
結束頁: 108069
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