Adaptive Iterative Learning Control in Optimization of Industrial Process

Iterative-learning control designed by adaptive control is used for the dynamics in the stable state optimization of nonlinear industrial process. The structure and the algorithm of adaptive iterative learning control are given, also the convergence of the algorithm and the stability of the closed-loop systems are proved. The problems of convergence rate, initial value and the selection of target trajectory are discussed. The tracking for multiple targets of different type can be achieved. The simulation shows that the dynamic performance of the system is remarkably improved.