On The Key Issues Of Adaptive Terminal Iterative Learning Control

In this paper,the key issues of random initial conditions and the iteration-varying reference trajectories have been explored and a series of new adaptive terminal iterative learning control methods have been proposed accordingly.The main innovations and contributions of this work can be summarized as follows:First,considering a class of nonlinear single-input single-output(SISO)discrete-time systems,by adding the forgetting factor to the controller,achieves the purpose of providing control performance.In our work,the boundedness of parameters is proved,and we also give an analysis of the asymptotic convergence of input,output,and tracking error.At the same time,the simulation results show that the controller with forgetting factor has better control performance.Second,for a general multi-input multiple-output(MIMO)linear time-varying system,the stochastic high-order internal-mode method is proposed to deal with the initial conditions of iteration-varying.In the study,the expected points are also varying with iteration.Thus,the proposed method overcomes the limitations of the traditional iterative learning control requirements that satisfy the same initial conditions and target trajectories.The rigorous theoretical analysis proves the feasibility of the scheme.In the simulation,by comparing the control method proposed with the same initial conditions,effectively validates the advantages of the proposed adaptive terminal iterative learning control method based on high order internal model.Third,the method of high order internal model is furthermore extended to nonlinear systems.The derivation is made directly by the nonlinear system and a new iterative learning controller based on higher-order internal model is designed by using internal model theory.The controller does not contain the information of the controlled system model,but only the use of measurable input and output data.Therefore,the proposed control method is a data driven control method.By rigorous mathematical analysis,it is proved theoretically that the proposed method is stable and can make the tracking error converge to zero asymptotically.The random disturbance is added to the simulation study,which further validates the validity and practicability of the proposed scheme.