Study On Iterative Learning Control Methods For Non-Strictly Repetitive Systems

With the development of iterative learning control in recent years,iterative learning control has been extensively applied in repetitive systems for path tracking.In practice,the strict repeatability of the system is affected by its or external interference.Thus,the system needs to relearn the altering information in each iteration.As a result,its robustness and tracking effect become increasingly difficult to verify.In this study,corresponding strategies are designed to solve iterative learning control problems for non-repetitive systems.Convergence analysis is carried out for initial shift,time delay,and desired trajectory,which all vary in each iteration.Finally,the effectiveness of the proposed control strategies are verified by simulation experiments.The main works and contributions in the dissertation are summarized as follows:(1)Time delay is a critical factor for the non-repetition of the system.This study aims to solve the trajectory tracking problem of the Caputo fractional-order linear time delay system.Accordingly,two states of input time delay and state time delay are analyzed,and the PD? type iterative learning law and the P?Convolutional type iterative learning law are proposed.The convergence of the iterative learning law is derived in the frequency domain,and the convergence range of the learning law controller is determined.The effectiveness of the proposed method in the time delay system is verified by numerical simulation.The effect of time delay on the system convergence speed is shown through simulation.(2)For the trajectory tracking problem of conformable fractional order nonlinear systems with initial value shift,a fractional order D? type iterative learning controller with initial state learning is developed.The proposed method breaks the restriction that the system must have the expected initial state.The system convergence is guaranteed,the influence of initial state error on the system is eliminated,and the robustness of trajectory tracking is improved.H(?)lder's inequality is extended to the fractional order domain in the convergence analysis.The convergence analysis of the proposed iterative learning law is carried out in the time domain.Finally,strict theoretical analysis and numerical simulation prove the effectiveness of the proposed method.(3)In order to solve the trajectory tracking problem with the iterative varying desired trajectory and initial value,a forgotten factor type iterative learning control strategy with initial value repaired is proposed in this paper.The convergence of the proposed control strategy is determined in the time domain.The effect of the initial error on the system is repaired and the desired trajectory is tracked effectively.Finally,the effectiveness of the control strategy is verified by numerical simulation.(4)The iterative learning control simulation experiment of robot non-strictly repeated trajectory tracking is completed.The trajectory tracking model of robot iterative learning control under the dynamic perspective system is constructed.The iterative learning law is developed using the system and the trajectory errors of world objects and imaging trajectory errors.The convergence is determined in the time domain.Aiming at the non-strict repetition problem caused by some factors,such as the initial value,system disturbance,and time delay,the iterative learning control law based on the dynamic perspective system is used to complete an accurate tracking control of robot trajectory in the simulation environment.