Intelligent control of electroactive polymer actuators based on fuzzy and neurofuzzy methodologies

Conjugated conducting polymer actuators, especially those based on polypyrrole (PPy), possess enormous potential for the creation of biomimetic devices, single-cell manipulators, numerous biomedical applications as well as robotics and prosthetics. This is due to their low actuation voltage, ability to operate at the macro- or microscale, large force-to-weight ratio, biocompatibility, low cost and their operation in aqueous and nonaqueous environments. This paper experimentally investigates the potential of intelligent control methodologies to improve the positional accuracy and response speed of trilayer PPy actuators. Two intelligent control techniques were designed and implemented - fuzzy logic PD+I control and neurofuzzy adaptive neural fuzzy inference system (ANFIS) control, which are fundamentally model-free control techniques. The performance of these controllers was compared to that of a conventional proportional integral derivative (PID) controller. It was found that the two intelligent control schemes significantly outperformed the conventional PID controller in both step and dynamic responses, with an improvement in rise time of at least 18 times and in settling time of at least two times. This study is the first to implement and compare fuzzy logic PD+I and neurofuzzy ANFIS PD+I intelligent control methodologies with a classical PID controller to the emerging field of conducting polymer PPy actuators and lays the groundwork for their use in functional devices.