Gain scheduled control of IPMC actuators with 'model-free' iterative feedback tuning

IPMCs are actuators with significant potential in robotics and biomedical applications. In order to make full use of their potential, their complex actuation behaviour must be effectively controlled. Previous efforts to control these actuators relied heavily on the development of an accurate model of the IPMC. After 15 years of modelling research, not one such accurate model exists. This paper presents a nonlinear controller which is adaptively tuned using a model free approach called iterative feedback tuning (IFT). Automatic tuning of the controller, without the any knowledge of the system is achieved. By eliminating the need for a plant model, the developed control system represents major progress towards the implementation of IPMCs into real world applications. The controller is tuned to achieve high position accuracy on both the micro and macro scale, where most previous controllers limit their operating range to remain in a small linear region. The proposed controller continuously adapts its parameters to handle the IPMC nonlinearity. The IFT algorithm has successfully tuned the control parameters with a performance increase of up to 65% from an initial arbitrary un-tuned controller. Experimental results show the newly proposed controller achieves much higher performance when compared with a conventional linear controller.