Research On ADRC Of PMSLM Based On Chaos Opposition-based Learning Invasive Weed Optimization Algorithm

Permanent magnet linear synchronous motor(PMSLM)has the advantages of high speed,high precision,fast response and so on.It is a kind of highly competitive direct drive motor.It has broad application prospects in the field of numerical control machine tools,military weapons and other occasions.However,due to the zero transmission of PMSLM,it is easy to be affected by multiple disturbances,which makes it more difficult to control.Auto disturbance rejection control(ADRC)technology is the biggest characteristic of "auto disturbance rejection",which can effectively restrain the influence of external disturbance on the control object,so it is very suitable for application in PMSLM control system.In this paper,the structure,the principle and the disturbance of the permanent magnet synchronous linear motor are analyzed.On this basis,the mathematical model of PMSLM is established,and the simulation model is built in MATLAB/Simulink.In view of the existing system output measurement noise in the actual system,ADRC is improved on the traditional,joined the tracking differentiator with differential predictive compensation algorithm in the feedback channel,by measuring the noise output to filter system.At the same time,the nonlinear function of bas(e,a,δ)is used to replace the fal(e,a,δ)function of the traditional auto disturbance rejection controller,so as to improve the control performance of the ADRC.Then the current decoupling controller of PMSLM is designed.The simulation results show that the improved ADRC is better than the traditional ADRC controller.According to the mathematical model of PMSLM,we firstly give the first-order speed ADRC controller.In order to make up for a lack of first-order speed controller,and the design of two-order speed ADRC controller.Simulation results show that the two-order speed control effect of ADRC is better than the first-order velocity control and PID control.Finally,invasive weed optimization(IWO)is introduced to solve the problem that the parameters of two-order ADRC are difficult to be adjusted.Considering the slow convergence speed of IWO algorithm,an chaos opposition-based learning invasive weed optimization(COLIWO)is proposed to realize the parameter tuning of ADRC.The simulation results show that the ADRC optimized by COLIWO has the advantages of no overshoot,fast response,strong anti-interference ability and so on.