Extension of learnable bandwidth for iterative learning control

This paper describes frequency domain formulation of iterative learning control systems and study on their convergence along the operation axis. The concepts of learnable bandwidth and monotonic convergence are addressed and analyzed. It is shown that learnable bandwidth is a critical indicator for monotonic convergence and performance quality of the learning process. To achieve the good learning, various solutions are proposed to tune this learnable bandwidth along operation. There are two approaches, off line and online tuning along operation repetition axis. In this paper, some approaches to extend the learnable bandwidth in various domains are discussed. Experimental results for these approaches show the potentials and effects of learnable bandwidth tuning. Some open problems are provided as well.