Optimization Iterative Learning Control Research Based On Co-evolution Shuffled Frog Leaping Algorithm

Iterative Learning Control(ILC)is a key branch of intelligent control and a new learning control strategy.The most important characteristic is that the input signal can be obtained by repeated application of previous control experience,producing the desired output to improve the control quality.Compared with other control methods,ILC has many advantages.It can be applied to non-linear systems with high uncertainties in a very simple way,and the calculation is relatively simple.ILC has high adaptability,which is easy to achieve in practical industrial control,significantly,the most prominent advantage of ILC is that it does not be dependent on accurate mathematical models.Overall,the study of iterative learning control has very important practical significance.In order to improve the convergence performance of nonlinear optimal iterative learning control algorithm and obtain better tracking performance,takes the advantages of the co-evolution shuffled frog leaping algorithm(CSFLA)to implement the performance optimization of ILC,co-evolution shuffled frog leaping algorithm based optimal iterative learning control is proposed.CSFLA can deal with the nonlinear problem and input constraints of ILC.The approach can reduce the search space and improve the convergence rate.Moreover,the proposed approach benefits from the design of a lowpass FIR filter.This filter successfully removes unwanted high frequency components of the input signal,which are generated by CSFLA algorithm method due to the random nature of CSFLA algorithm search.Compared optimal iterative learning control based on genetic algorithm with iterative learning control based on clonal selection algorithm,optimal iterative learning control based on co-evolution shuffled frog leaping algorithm is superior to both of them in convergence speed.The traditional iterative learning control algorithm in selecting learning parameters has significant impact on convergence and convergence rate.In the traditional PID iterative learning control,the settings of learning parameters rely on experience with certain blindness,the convergence conditions given by algorithm analysis cannot be used in the selection of learning parameters.An optimal iterative learning control based on PID co-evolution shuffled frog leaping algorithm is proposed.The algorithm can still get the PID parameters,which lets system convergemonotonously in the condition that is not satisfy the monotone convergence of PID iterative learning control.Finally,optimization iterative learning control algorithm based on co-evolution shuffled frog leaping algorithm is applied to the crane-pendulum system,simulation results verify the validity and feasibility of the algorithm.