Adaptive Iterative Learning Control For Permanent Magnet Synchronous Motor Servo System

Permanent magnet synchronous motor(PMSM)has been widely used in the field of industrial robot,high precision CNC machine tools and so on because of its small volume,simple structure,low inertia and high power density.Iterative learning control(ILC)is suitable for servo control systems that perform repetitive tasks and can achieve the perfect tracking effect in theory.However,the uncertainties in the actual system,such as various kinds of disturbances,modeling error and parameter time-varying,will have an adverse effect on the convergence of the ILC and the tracking performance of the system.Therefore,owing to the defaults of PMSM servo system in the ILC process in dealing with uncertaines,the adaptive control is added into the ILC control called adaptive iterative learning control(AILC).AILC has double advantages of ILC in solving the problem of repeated tracking and adaptive control in solving the problem of system uncertainties,the tracking precision of PMSM servo system is improved,and the convergence rate of the system is accelerated.Firstly,the structure and classification of PMSM are introduced,and according to the coordinate transformation,the mathematical model of PMSM in dq coordinate system is established.The PMSM vector control system is established by using id=0 control strategy.The influence of the uncertain factors such as friction torque,cogging torque,model error and time varying parameter on the control system is analyzed,and the state equation of the system is established.Secondly,aiming at the problems shch as the error is difficult to converge and the tracking accuracy is reduced due to the PMSM servo system is affected by the uncertainty of the model,a parameter adaptive iterative learning law is proposed.It is mainly based on the PD feedback control to increase the adaptive iteration and the unknown parameters of the control law are identified by learning along the iterative axis.Then an improved adaptive iterative learning law is proposed,equivalent to on the basis of the previous learning law,the time domain estimation of unknown parameters is increased,and the information of the time domain and the iteration domain is fully utilized.The convergence of the two schemes is analyzed based on Lyapunov stability theory.Simulation results demonstrate that in the presence of model uncertainty,AILC has faster convergence rate and higher tracking accuracy compared with conventional type ILC,and it could improve the performance of the system effectively.Finally,to solve existing problems of dynamic parameter uncertainty in the operation of PMSM servo system,a method of combining L1 adaptive control with ILC is proposed.L1 adaptive controller is used to deal with dynamic parameter uncertainty and compensate for additional perturbations in the time domain so that the learning controller can be designed on a nominal system.The learning controller is designed to improve the tracking ability of the system compensate for repetitive system uncertainty such as friction disturbance,cogging troque.Simulation results illustrate that the proposed scheme has a good effect on reducing the influence of dynamic parameter uncertainty to system and improving the performance of the system.