Research On PMLSM Direct Thrust Force Control System Based On Fractional Order Complementary Sliding Mode

In recent years,permanent magnet linear synchronous motor（PMLSM）is widely used in high-speed and high-precision servo system with the advantages of high efficiency,large thrust and fast response.The PMLSM direct thrust force control system has excellent dynamic response ability,but has large thrust fluctuation and steady-state error,and the speed tracking performance is easily affected by uncertainty,using traditional control methods cannot guarantee the system has good robust performance and tracking accuracy.In order to improve the speed tracking ability of the PMLSM direct thrust force control system,and improve the control quality of the system under the influence of uncertainty,an intelligent fractional order complementary sliding mode speed controller combined with a recurrent RBF neural network is designed to ensuring the fast response of the PMLSM direct thrust force control system,the speed tracking accuracy and robust performance of the system are improved.Firstly,the research status and application prospect of PMLSM servo system are reviewed,and the principle and structure of PMLSM are expounded.Then,the dynamic PMLSM mathematical model including uncertainty is established,the direct thrust force control system of PMLSM is built,and the influence of uncertainty on tracking performance is analyzed.Then,aiming at the problems of insufficient control accuracy in the PMLSM direct thrust force control system,a fractional order complementary sliding mode control method is proposed.Using the strong convergence of complementary sliding mode control and the filtering characteristics of fractional order calculus operator,the chattering in sliding mode control and the thrust pulsation in direct thrust force control system are suppressed,the speed steady-state error is reduced,and ensure that the system has good robust performance and speed tracking accuracy.The stability of the method is proved by using fractional order system theory and Lyapunov theorem,and the effectiveness of the method is proved by simulation comparison.Finally,in order to improve the control performance of the system under the influence of uncertainty,and further improve the dynamic response capability and control accuracy of the system,an intelligent fractional order complementary sliding mode control method based on recurrent RBF neural network is proposed.Using recurrent RBF neural network estimator and adaptive compensator to replace the switching control term,the difficulty in choosing the sliding mode gain of the saturation function and the thickness of the boundary layer is resolved,and further improve the control quality of the system.The comparison of simulation results shows that this method can effectively improve the dynamic response ability of the PMLSM direct thrust force control system,reduce the tracking error,and further improve the speed tracking ability and robust performance of the system under the influence of uncertainty.