An intelligent controller for ionic polymer metal composites using optimized fuzzy reinforcement learning

Abstract

This paper proposes an optimized fuzzy reinforcement-learning algorithm to control ionic polymer metal composites. The IPMC has been made by thin polymer membrane with metal electrodes plated chemically on the both faces. Its application is widely and may be used as the artificial muscle due to the large bending strain at low voltages. Although there are some controllers designed in the literature, most of them are model-based and for this reason are not used widely. In this study, a free model controller based on fuzzy is considered. The fuzzy rule making is not straightforward and must be taken by an expert, so an algorithm based on the reinforcement learning is employed to make the rule sets strongly. After learning the fuzzy sets, firstly, the reinforcement learning parameters have been optimized using the Taguchi method and then an optimized algorithm based on the genetic is started to tune up the configuration of membership functions for controller designing. The effectiveness of the reported controller for the IPMC actuator is confirmed by simulation and experimental results.