Research On Variable Gain Iterative Learning Control Algorithm With Forgetting Factor

Iterative learning control is an important branch of intelligent control.The control process is mainly through constantly and repeatedly correcting the control input,and finally making the control output signal completely track the desired trajectory.Among many control methods,the principle of iterative learning control is simple and easy to understand,and the control algorithm is easy to implement,which has attracted the attention of many scholars at home and abroad.This paper first introduces the research direction and status quo of iterative learning control at home and abroad,briefly summarizes the research content and methods of iterative learning control,and then gives a study of variable gain iterative learning control algorithm with forgetting factor.The main research contents are as follows:1.In order to improve the shortcomings of large system tracking error caused by the fast convergence rate of iterative learning control with variable gain,a variable forgetting factor is introduced in variable gain iterative learning control,and the forgetting factor is set as a function of the number of iterations.In the linear system,D-type iterative learning control is used to design variable gain iterative learning control with forgetting factor,and the convergence of learning law is analyzed.Iterative learning control with forgetting factor and variable gain iterative learning control are compared respectively by MATLAB.The simulation results show that the variable gain iterative learning control with forgetting factor can not only improve the iteration convergence speed,but also significantly improve the tracking error stability.The restrictions on the initial input have been weakened.2.In order to solve the difficulties of iterative learning control in the selection of gain parameters,based on the variable-gain iterative learning control with forgetting factor,a parameter optimization method is combined.In a linear discrete system,a variable-gain iterative learning control law with forgetting factor is used to design a parameter optimization constraint formula for the learning law so that each iteration is solved according to the optimization problem.Solve the parameter optimization solution and prove that the parameter is the optimal solution.The MATLAB simulation experiment was designed to compare the variable gain iterative learning control with forgetting factor and the improved iterative learning control with single parameter optimization.The simulation results show that the improved iterative learning control based on parameter optimization can significantly improve the performance of iterative learning control.3.For the manipulator system,when the control system is nonlinear and has system disturbances,the self-adaptive idea is introduced in the variable-gain iterative learning control with forgetting factor,and the convergence law of the learning law is analyzed according to the Lapunov stability theorem.MATLAB simulates and contrasts PID adaptive iterative learning control.The simulation results show that the improved adaptive iterative learning control in this paper improves the error stationarity and convergence speed,and has good robustness against system disturbances.