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为了提高异步电动机的控制器准确度,针对传统PID控制存在的一些不足,提出了一种改进神经网络优化PID和模糊理论的异步电动机控制策略。首先采用神经网络对异步电动机PID控制器的三个参数进行实时、自适应调整,并通过改进粒子群算法优化神经网络参数;然后利用模糊控制器替代异步电动机的滞环控制器;最后通过仿真实验对其性能进行测试。实验结果表明,该控制策略大幅度改善了异步电动机控制器的动态响应性能,具有较好的鲁棒性,且实际应用价值更高。
In order to improve the accuracy of asynchronous motor controller, aiming at some shortcomings of traditional PID control, an improved asynchronous motor control strategy based on neural network optimization PID and fuzzy theory is proposed. First of all, the three parameters of asynchronous motor PID controller are adjusted in real time and adaptively by neural network, and the parameters of neural network are optimized by the improved particle swarm optimization algorithm. Then the fuzzy controller is used to replace the hysteresis controller of asynchronous motor. Finally, Test its performance. Experimental results show that this control strategy can greatly improve the dynamic response performance of asynchronous motor controller with better robustness and higher practical value.