基于自适应PID控制器的异步电机矢量控制

Asynchronous motor vector control based on self-adaptive PID controller

为了克服传统PID控制器自适应性及鲁棒性相对较差的缺点,实现高性能的异步电机矢量控制,提出了采用人工神经网络技术构造自适应PID控制器,在保证调速系统全局快速收敛的情况下,运用有监督的Delta学习规则和合理的控制算法,实现自适应PID控制器参数的在线自动调整。应用MATLAB软件设计基于自适应PID控制器的异步电机矢量控制模型并进行仿真研究,结果表明,自适应PID控制器不仅能够满足异步电动机矢量控制的实时性要求,而且可以大大改善异步电动机的动态性能与静态性能,表现出较强的自适应性与鲁棒性,因而可以取代传统PID控制器以实现高性能的异步电动机矢量控制。

A self-adaptive PID controller based on artificial neural network technology is brought forward to overcome the shortcomings of the traditional PID controller that is relatively weak in self-adaptability and robustness, and to achieve a vector control by an asynchronous motor with high performances. The supervisory Delta study rules and reasonable control algorithms were adopted to automatically fulfill the on-line parameter adjustment of the self-adaptive PID controller in the assurance of an overall and fast convergence of the asynchronous motor driving system. The MATLAB software was used to establish the asynchronous motor vector-controlling model based on the self-adaptive P1D controller to carry out a simulation investigation. The results show that the self-adaptive PID controller, which has relatively high self-adaptability and robustness, can satisfy the requirement for time-reality of the asynchronous motor vector control, and greatly improve the dynamic and static performances of the asynchronous motors. Thus the traditional PID controllers can be substituted to realize asynchronous motor vector-control with high performances.