Adaptive neuro-fuzzy control for ionic polymer metal composite actuators

Abstract

Electroactive polymers (EAPs) have many attractive characteristics for applications, especially for biomimetics robots and bio-medical devices. Among the electroactive polymers, the ionic polymer metal composite (IPMC) is the commonly used EAPs. The IPMC is new generation of smart materials with significant potential in producing biomimetic robots and smart structures, and for medical applications. Ionic polymer metal composites (IPMC) have attracted great attention in the past years due to its large strain. IPMC materials have quite an unpredictable behavior due to several critical aspects that produce a change in their dynamic response. For modeling of controlling the IPMC, it is required to find suitable algorithm. In order to avoid difficult problems in control, a controller based Adaptive Neuro-Fuzzy Inference System (ANFIS), which combines the merits of fuzzy logic and neural network, is used for tracking position of IPMC actuator. This paper describes the using of controller based on Neuro and Fuzzy for controlling an IPMC actuator under water to improve tracking ability for an IPMC actuator like as biomimetics and biomedical devices. The results showed that ANFIS algorithm is reliable in controlling IPMC actuator. In addition, experimental results show that the ANFIS performed better than the pure fuzzy controller (PFC). Present results show that the current adaptive neuro-fuzzy controller can be successfully applied to the real-time control of the ionic polymer metal composite actuator for which the performance degrades under long-term actuation.