Abstract：Through the analysis of experimental results of prototyped switched reluctance motor (SRM), the effect of its speed, phase current, turn-on angle and turn-off angle on torque are introduced firstly. A SRM control system with optimal current mode is realized based on the foundation of its static multivariable neural network controller and the design of its training method to train the neural network secondly. To obtain training data of neural network efficiently, a variable step-size optimization method based on least square method is proposed. Current optimal control system can be realized to make output maximum torque by adjusting the turn-on and turn-off angle. Then the presented neutral controller is combined with the normal PID controller to achieve its ability of dynamic adjustment. Finally, the effectiveness of the proposed design is demonstrated through prototype SRM experiments.
张云, 徐衍亮, 孔辉, 李元东. 电流最优的多变量静态神经网络开关磁阻电机控制[J]. 电工技术学报, 2013, 28(8): 250-258.
Zhang Yun, Xu Yanliang, Kong Hui, Li Yuandong. Switched Reluctance Motor Control Based on Multivariable Neural Network with Optimal Phase Current. Transactions of China Electrotechnical Society, 2013, 28(8): 250-258.
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