A New Fuzzy Iterative Learning Control Algorithm for Single Joint Manipulator

Abstract

This paper present a new fuzzy iterative learning control design to solve the trajectory tracking problem and performing repetitive tasks for rigid robot manipulators. Several times' iterations are needed to make the system tracking error converge, especially in the first iteration without experience. In order to solve that problem, fuzzy control and iterative learning control are combined, where fuzzy control is used to tracking trajectory at the first learning period, and the output of fuzzy control is recorded as the initial control inputs of ILC. The new algorithm also adopts gain self-tuning by fuzzy control, in order to improve the convergence rate. Simulations illustrate the effectiveness and convergence of the new algorithm and advantages compared to traditional method.

Archives of Control Sciences

“…In this paper, Q-learning technique is suggested to design self-improving controller as the most promising attempt with relatively many reported initial studies. In a sense, this approach can be considered as similar to Iterative Learning Control (ILC) that is widely used in automatic and robotics for batch processes [3,20]. Different applications of Q-learning in automation have been already reported.…”