A Kind of Learning Gain Design Method for Energy-Function-Based Iterative Learning Control

A new approach of energy-function-based iterative learning control (EF-based ILC) with auto-tuning learning gains is proposed in this paper. In the proposed algorithm, the learning gain of the EF-based ILC scheme is iteration-dependent, i.e. the learning gain is adjusted after every iteration based on a fuzzy logic controller (FLC). The incorporation of FLC into ILC provides the learning gain with the capability of changing itself by evaluating the previous learning results. As a consequence, both efficient learning process and learning convergence can be guaranteed with less system information. Moreover, the proposed fuzzy-rule-based tuning methodology offers a systematic way of learning gain design and greatly facilitates real-time implementations of EF-based ILC algorithms