Research On Error Trajectory Tracking Control Based On Adaptive Iterative Learning Algorithm

Most of the controlled objects usually have the characteristics of strong nonlinearity strong coupling and uncertainty,which will lead to the lack of accurate model of the systems.Adaptive theory and iterative learning theory are combined into adaptive iterative learning mechanism.An ideal control input signal is searched by the previous control and the current tracking error.It is of great importance to study the nonlinear system with strong nonlinear,strong coupling and uncertainty.In the first chapter,the research background and research meaning of this thesis are described in the first chapter.In second chapter,the basic knowledge of adaptive iterative learning control and mathematical theory are introduced to provide the theoretical basis for the controller design in the later chapter.In the third chapter,aiming at the systems with uncertain parameters and any iteration initial values,by utilizing the given correction reference signal construction method,the initial correction path is designed which corresponds to the mission path.Based on the Lyapunov function,an adaptive iterative learning controller of exponential varying gain is designed,the reference signal are accurately tracked ultimately in the task interval through a certain number of iterations.According to the drawback that the different reference signals require the design of different initial correction signals,a constructing desired error trajectory scheme is proposed and the error trajectory tracking algorithm is given,which can achieve effective tracking in non uniform task trajectory,two examples are given to verify the validity of the proposed method.In the fourth chapter,in the above chapter,the initial value of constructing correction reference signal and the expected error signal are equal to the real initial value of systems.While,this is difficult in the real system,which will degrade the tracking effect and slow down the tracking speed.In order to solve this problem,an adaptive iterative learning controller of exponential varying gain with initial correction acceleration and an adaptive iterative learning error tracking controller of exponential varying gain with initial correction acceleration are designed,meanwhile,the time-varying initial correction functions are introduced to correct the error functions of the proposed two methods.In the fifth chapter,adaptive iterative learning controllers are designed for the dynamic model of manipulator with uncertain disturbances and unknown parameters,under the cases of fixed initial iteration value and the initial values satisfying the Alignment condition,respectively.Based the proposed methods,apply the thought of correction acceleration of the initial segment and the tracking of error trajectory to the above mentioned models,and the simulation results illustrate the effectiveness.Finally,summarize the main content of this thesis and point out the further research directions.