Research On Complementary Sliding Mode Control Strategy For High Precision Permanent Magnet Linear Synchronous Motor

As the core unit of direct transmission mechanism,due to its high speed,high precision and high efficiency,PMLSM is widely used in high-end manufacturing fields such as high-end CNC machine tools,microelectronics equipments,precision measurements and IC core manufacturing,which has a wide application prospect.However,owing to the elimination of the intermediate mechanical transmission links in structure,the uncertainty factors such as parameter variations,load disturbances and friction forces will directly act on the mover of the motor,which increases the difficulty of electrical control,and directly affects the performance of high-precision CNC machining system.Therefore,in the field of high precision micro feed control,and under the premise of considering the influence of uncertaintites on the system,it is necessary to stand at a high level to study the control strategy of linear motor servo system,which is of great significance for theoretical analysis and engineering practice.Aiming at high-speed and high-precision machining,this dissertation takes PMLSM as the research object,and focuses on solving the problem that it is easy to be affected by uncertain factors and reduce the servo performance.Based on sliding mode control(SMC),this dissertation combines backstepping control and neural network control to study the position tracking of linear servo system,which takes into account the dual requirements of robustness and tracking performance of high-end CNC machine tools for high-precision servo system.The main research contents are shown as follows:(1)Based on the analysis of the basic structure and working principle of PMLSM,the voltage,flux,electromagnetic thrust and motion equation of PMLSM are analyzed and deduced.The electromechanical coupling system model with uncertain factors including parameter variations and external disturbances is established.The uncertain factors affecting the servo performance of motor are analyzed,which provides theoretical basis for the analysis and overall design of the control system.(2)Aiming that the problem that PMLSM servo system is easily affected by parameter variations and load disturbances,on the SMC,complementary sliding mode control(CSMC)method is designed by introducing complementary sliding surface to overcome the influence of uncertain factors on the system and improve the position tracking accuracy of the system.At the same time,in order to solve the problem of poor robustness in the fixed boundary layer of CSMC,the global CSMC is designed by introducing the concept of approach angle to optimize the boundary layer.Under the premise of not affecting the fast response and tracking performance,the method effectively weakens the chattering and improves the robustness to uncertainties.Simulation results show that compared with SMC and CSMC,the global CSMC can effectively reduce the position tracking error and improve the tracking accuracy of the system.(3)In order to improve the global stability and the complete robustness of the system,and solve the problem of difficult selection of controller parameters,an adaptive backstepping CSMC method is proposed,which combines the backstepping control theory,the second-order sliding mode control idea and CSMC to ensure the position tracking performance of PMLSM servo system.By using position error and virtual variable error to design sliding mode surface,the adaptive backstepping second-order CSMC method not only inherits the advantages of global stability of backstepping control and complete robustness of second-order SMC,but also has the advantage of reducing tracking error by half of CSMC.In addition,in order to solve the problem that the upper bound value of uncertain factors is difficult to select,the adaptive law is designed to estimate the uncertain factors of the system and adjust the controller parameters online.The simulation results show that the method is feasible and effective,and can improve the position tracking accuracy of the system,and has strong robustness to the uncertain factors.(4)In order to estimate the uncertain factors,and further improve the servo performance of PMLSM system,an intelligent backstepping second-order CSMC method based on Gegenbauer recurrent fuzzy neural network(GRFNN)and whale optimization algorithm(WOA)is designed to improve the tracking performance of the system for different reference trajectories.On the basis of adaptive backstepping second-order CSMC,GRFNN is used to replace the original adaptive law,which is used to approximate the uncertainties of the system,feed back the dynamic information in real time,and avoid the problem that the optimal performance can not be guaranteed by selecting the controller parameters empirically.At the same time,WOA is used to optimize the connection weights and speed up the learning rates of neural network,combined with offline training and online learning,to solve the problem of neural network online training affecting the dynamic performance of the system,and further improve the servo performance of the system.The simulation results show that the intelligent backstepping second-order CSMC method has obvious advantages in improving the position tracking accuracy and robustness of the system.(5)Finally,a PMLSM system experiment platform based on links-RT is established to verify the effectiveness and feasibility of the proposed control algorithm.Links-RT is a real-time simulation test equipment based on real-time simulator and motor,supplemented by software and hardware configuration,which has high reliability and strong real-time.In this dissertation,two linear motors are used to carry out the loading experiment,and the nominal parameter experiment,parameter variation experiment and variable load experiment are carried out for the proposed control schemes in this dissertation.The experimental results verify the feasibility and effectiveness of the proposed method.