The iterative learning control and convergence analysis for nonlinear industrial process control systems

Abstract

In the paper, based on hierarchical control structure in steady-state optimization of industrial processes and iterative learning control law for linear industrial process control systems, the iterative learning control is applied to saturated nonlinear industrial control systems and nonlinear industrial control systems with changing gains. The weighted PD-type closed-loop iterative learning control algorithm and weighted power-type open-closed-loop iterative learning algorithm are discussed respectively. The definitions of /spl delta/-reachability of objective trajectory and /spl epsi/-convergence of the iterative learning control algorithm are suggested. By means of Bellman-Gronwall inequality and /spl lambda/-norm theory, the convergence of the algorithms is also proved. The numerical simulation shows that the iterative learning control can remarkably improve the dynamic performance of industrial control systems in steady-state optimization.