永磁同步电动机的自适应神经网络动态面控制  

Adaptive Neural Networks Dynamic Surface Control for Permanent Magnet Synchronous Motor

作　　者：于洋[1] 王巍[1] 

机构地区：[1]辽宁工业大学电气工程学院,辽宁锦州121001

出　　处：《计算机仿真》2014年第10期401-404,444,共5页Computer Simulation

基　　金：国家自然科学基金项目(61074014);辽宁省教育厅科学研究一般项目(L2013244);辽宁工业大学教师科研启动基金(X201313)

摘　　要：研究永磁同步电动机的位置跟踪控制问题。针对参数不确定的永磁同步电动机系统,提出自适应神经网络动态面位置跟踪控制方法。根据Stone Weierstrass逼近定理,利用神经网络逼近电动机系统中的复杂非线性函数。采用动态面技术的自适应反步方法设计电动机的位置跟踪控制器实现电动机的位置跟踪控制。提出的控制策略不仅能够克服电机参数的不确定性和负载扰动,而且避免了传统反步设计方法存在的"复杂性爆炸"问题。根据Lyapunov稳定性理论,证明闭环系统具有半全局稳定性,位置跟踪误差收敛于原点的小邻域内。仿真结果表明了所提控制方法能够使电动机快速、准确地跟踪给定的位置信号;神经网络能够很好地逼近系统中的复杂非线性函数。This paper considers the of position tracking for permanent magnet synchronous motors. An adaptive neural networks dynamic surface position tracking control method was proposed for permanent magnet synchronous motors with parameter uncertainties. Based on Stone Weierstrass approximation theorem, neural networks were uti- lized to approximate the complex nonlinear functions. An adaptive backstepping control method based on dynamic surface control technique was proposed to guarantee the position tracking performance in the presence of parameter uncertainties and load torque disturbance. In addition, the developed control scheme can overcome the problem of ＂explosion of complexity＂ in the backstepping design. Based on Lyapunov stability theory, the closed- loop system was semi -globally uniformly ultimately bounded, and the position tracking error converged to a small neighborhood of the origin. Simulation results verify that the proposed control method guarantees that the permanent magnet syn- chronous motors can track a given reference signal quickly and accurately. Besides, the neural networks have good approximation performance to the complex nonlinear functions.

关 键 词：永磁同步电动机 位置跟踪控制 自适应控制 神经网络控制 动态面控制 

分 类 号：TM351[电气工程—电机]