摘要
针对永磁同步电机提出一种基于反演的PMSM自适应滑模控制方案。设计基于反演的滑模变结构位置控制器,通过RBF神经网络实现系统参数变化和外部负载扰动等引起的不确定上界值的在线辨识,减小滑模控制器的控制量,并引入饱和函数来减弱系统的"抖动"现象。理论分析和仿真结果对比表明,基于RBF神经网络的自适应反演滑模控制对参数变化和外部负载扰动具有很好的鲁棒性,永磁同步电动机获得了很好的跟踪效果。
Backstepping based adaptive sliding-mode control scheme for permanent magnet synchronous motor（PMSM） is proposed. A backstepping based sliding-mode variable structure controller is designed. The RBF neural network is used to estimate the upper bound of uncertainties in real-time, which includes parameter variations and external load disturbance, then the control effort of the slidingmode controller is reduced. To reduce the chattering phenomenon, the sign function in sliding-mode controller is replaced by the satu- ration function. Theoretical analysis and simulation results show that the adaptive sliding-mode control based on RBF neural network is robust to uncertain parameters and load torque disturbance, and the PMSM system achieves excellent tracking performance.
出处
《控制工程》
CSCD
北大核心
2009年第1期49-51,共3页
Control Engineering of China
关键词
永磁同步电机
逆向递推
滑模控制
自适应控制
RBF神经网络
permanent magnet synchronous motor
Backstepping
slidingmode control
adaptive control
RBF neural network
