Effective motion control of the biomimetic undulating fin via iterative learning

The biomimetic undulating fin, RoboGnilos, is inspired by natural fish that generally swim via undulations of a long dorsal or anal fin. However, the present performance of this fin-type underwater propulsor can hardly be satisfactory in velocity, efficiency, or maneuverability, and retains a long distance to practical applications. This paper examines the dynamics of the undulating fin, and proposes an iterative learning approach based motion control to improve its steady propulsion velocity. This iterative learning controller is cooperated with a filter, to reduce the measurement noise, and a curve fitting component, to keep the necessary phase difference between neighbored fin rays. The detailed iterative learning based motion control algorithm is designed and implemented in the biomimetic undulating fin. The experimental results validate that the proposed learning motion control can effectively improve the propulsion of RoboGnilos. For instance, the steady propulsion velocity may be enhanced by over 40% with specified parameters.