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.
关键词
迭代学习/数据驱动/范数最优/运动控制
Key words
iterative learning/data drive/norm optimal/motion control
维普
万方数据