The iterative learning control for the course of steady-state hierarchical optimization of linear large-scale industrial processes

The weighted open-loop and closed-loop PD-type iterative learning control algorithms are suggested for linear large-scale industrial processes control systems to improve the transient response performance of the system. The basic iterative learning control structure is established. The ε-convergence of the algorithms is defined and proved. Digital simulations indicate the effectiveness of the iterative learning control algorithms, such as shorten the settling time and decrease the overshooting.