Flow Adaptive Speed Control of Underwater Bionic Propulsion via Adaptive Iterative Learning Control

Fish have gained excellent swimming ability from the natural evolution. The great swimming skills of fish are largely derived from the effective interaction with the surrounding flow field, using the sensory organs such as the lateral line system to obtain the flow information and further to achieve effective control of the swimming behavior. This paper makes effort on flow adaptive speed control of underwater bionic propulsion via adaptive iterative learning control. The kinetics and dynamics of underwater bionic propulsion are discussed first of all, then the flow adaptive control loop is introduced. As a feasible way, the adaptive iterative learning control (AILC) is used to deal with the cruising speed control problem under the condition that the flow velocity is perceptible. The control performance under the perception of the flow velocity is comparatively simulated and analyzed. The results show that the perception of the flow velocity is significant to the improvement of the control performance. Adaptive iterative learning control can achieve satisfactory control effect on the bionic cruising control with uncertain term and periodic disturbance. The research has both theoretical and practical significance for developing a practical and efficient bionic underwater propulsion device and its control system.