Research on Norm Optimal Iterative Learning Control Based on Data Driven
The traditional norm optimal iterative learning control(NOILC)can effectively improve the tracking accuracy of the servo system under the premise of determining the system model.However,in the actual control process,the system model parameters are often changed,resulting in a decline in the per-formance of the controller.Therefore,a data-driven norm-optimal iterative learning control method is pro-posed.Firstly,based on the input and output of the system,the cost function of the system estimation model is established.Then,the cost function is processed by partial differential,and a data-driven non-parametric model identification method is obtained.Finally,the model identification method is combined with NOILC.The experimental results show that for the time-varying system,the tracking error of this control method is 57.1％of NOILC,and it converges five times ahead of NOILC.Therefore,the proposed method can effectively improve the tracking performance of time-varying systems.
iterative learningdata drivenorm optimalmotion control
万方数据
维普
