Control Method Of Linear Motor Drive System Based On Parametric Feedforward Iterative Learning

In order to achieve high-speed and high-precision control goals,linear motor drive system is widely used in industrial automation equipment,and its control performance directly affects the quality of high-end equipment.However,the start/stop error at high speed severely limits the improvement of the positioning accuracy of the direct drive table.Based on this,this paper proposes a positioning error compensation strategy based on parametric feedforward controller,which has important scientific significance and engineering application value to effectively improve the positioning accuracy of linear motor drive system.According to the working principle of the linear motor drive system,the equivalent dynamic model of the linear motor drive system is constructed.Combined with the comparative analysis of control strategies,the advantages and disadvantages of different classical control strategies are clarified.Therefore,aiming at the non-minimum phase problem caused by the inverse feedforward control of the model,a parametric feedforward controller is obtained by means of the experimental data of the drive system,and it is combined with the optimization algorithm to make up for the dynamic tracking error caused by the uncertainty of the inverse model of the system,and the accurate compensation of the start/stop error of the linear motor drive system is realized.Considering the amplitude and rate of change of the input signal,the high robustness of parametric feedforward control is combined with the signal constraint ability of optimal iterative learning.The core idea of norm optimal iterative learning control is referred to,and a weighted matrix of signal constraint and signal change rate constraint is introduced to construct a feedforward control method based on parametric iterative learning.The problem of insufficient dynamic response of linear control strategy in fast start/stop and easy to cause overshoot oscillation is effectively reduced,and the convergence speed of parameter optimization process is optimized.Combined with the numerical analysis of different control strategies,it is verified that the proposed control algorithm can effectively improve the positioning accuracy of the system.According to the above theoretical modeling and analysis,the linear motor drive performance analysis experimental platform was built,and the performance comparison experiment of linear motor drive system under different control strategies was completed.It can be seen from the experimental results that compared with the parameterized feedforward control and optimal iterative learning control methods,the parametric feedforward optimal parameter tuning method proposed in this paper reduces the peak start/stop error by 70.175%and 78.788%,and the binary norm of start/stop error by 68.21%and 59.3%,respectively,after one iteration.After 10 iterations,the peak start/stop error decreases by 85.672%and 82.818%,and the twonorm start/stop error decreases by 69.8%and 68.69%,respectively.At the same time,the convergence speed of the proposed control strategy is better than that of the parametric feedforward control,and the robustness of the variable reference trajectory of the iterative learning process is obviously better than that of the optimal iterative learning.