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标题：基于RBF神经网络的永磁同步电机速度控制 |
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作者：强 勇，凌有铸，贾冕茜 |
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| 2013年第4期 访问次数：310次 |
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摘 要：针对常规PID控制的永磁同步电机调速系统性能不足，利用RBF神经网络较强的非线性映射能力，提出一种基于RBF神经网络PID自整定方案。该算法通过RBF神经网络在线辨识对PID参数整定，改善常规PID控制效果。在Matlab/Simulink构建了基于S函数的RBF神经网络PID控制器和永磁同步电机调速系统，并结合研究对象进行仿真研究。仿真结果表明：该控制器具有较好的静、动态性能，并有较强的自适应性和鲁棒性。
关键词：永磁同步电机；RBF神经网络；PID参数整定
Abstract: For the shortage of the traditional PID control of PMSM speed control system, in the advantage of strong nonlinear mapping ability of the RBF neural network，a kind of PID self-tuning algorithm based on RBF neural network was proposed in this paper. The effect of traditional PID control system was improved by using RBF neural network online identification of PID controller parameters in this algorithm. An S-function-based RBF neural network PID controller and PMSM speed control system was built in Matlab / Simulink, and study of research object was simulated. The simulation results show that the controller has better static and dynamic performance, and also has strong adaptability and robustness.
Key words: PMSM; RBF neural network; PID parameter tuning
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