基于神经网络的PMSM自适应滑模控制

结合滑模控制和神经网络各自的优点,对永磁同步电机(PMSM)提出了一种基于神经网络的PMSM自适应滑模控制方案.首先设计了带积分操作的滑模变结构位置控制器,通过递归神经网络的在线学习来实时估计系统参数变化和外部负载扰动等不确定性的界限,减小滑模控制器的控制量.进而,在滑模控制器中又引入饱和函数取代符号函数,进一步减弱"抖振"现象.理论分析和实验仿真对比研究的结果表明所提出方法具有优越的动态性能和鲁棒性.

Neural-network-based adaptive sliding mode control for PMSM

With the combination of the merits of sliding-mode control and neural network,a neural-network-based adaptive sliding-mode control scheme for permanent magnet synchronous motor (PMSM) is proposed.First,a sliding-mode controller with an integral-operation switching surface is designed.Then a recurrent neural network is used to estimate the upper bound of uncertainties in real-time,which include parameter variations and external load disturbance,such that the control effort of the sliding-mode controller is reduced.Furthermore,in order to reduce the chattering phenomenon,the sign function in sliding-mode controller is replaced by the saturation function.Theoretical analysis and experiment simulation results show that the proposed strategy has high-performance dynamic characteristics and stronger robustness.