基于智能分数阶互补滑模的PMLSM直接推力控制

Direct Thrust Force Control Based on Intelligent Fractional Complementary Sliding Mode for PMLSM

为提高永磁直线同步电机直接推力控制系统的速度跟踪能力,提出一种基于径向基函数(RBF)神经网络的智能分数阶互补滑模控制(IFOCSMC)方法。该方法结合分数阶微积分算子的滤波特性和互补滑模控制的强收敛性,削弱滑模控制的抖振问题,抑制直接推力控制系统中固有的推力脉动,提高系统的速度跟踪精度。再利用RBF神经网络对系统受到的扰动进行在线补偿,进一步提高系统的动态响应能力和抗干扰能力。通过Lyapunov稳定性判据验证了IFOCSMC方法的稳定性,仿真结果表明,该方法有效地抑制了抖振问题,减小了推力脉动,提高了系统的速度跟踪性能。

In order to improve the speed tracking ability of direct thrust force control system of permanent magnet linear synchronous motor(PMLSM),an intelligent fractional complementary sliding mode control(IFOCSMC)method based on radial basis function(RBF)neural network was proposed.The method combined the filtering characteristics of fractional calculus operator and the strong convergence of complementary sliding mode control,weakened the chattering problem of sliding mode control,suppressed the inherent thrust pulsation in direct thrust force control system,and improved speed tracking accuracy of the system.RBF neural network was used to compensate the disturbance on-line,so as to further improve the dynamic response ability and anti-interference ability of the system.The stability of IFOCSMC method was verified by Lyapunov stability criterion.The simulation results show that the method can effectively suppress the chattering problem,reduce the thrust pulsation and improve the speed tracking performance of the system.