Precise Speed Tracking Control of A Robotic Fish via Iterative Learning Control

摘要

In this paper, we present a novel work in which an iterative learning control (ILC) method is applied to a two-link Carangiform robotic fish in real time and achieves precise speed tracking performance. By virtue of the Lagrangian mechanics method, we establish a mathematical model for the robotic fish. The robotic fish model is highly nonlinear and nonaffine in control input, which hinders the applicability of most control methods that require affine-in-input. ILC is suitable because it works for such circumstances. A P-type ILC algorithm is adopted for speed tracking tasks of the robotic fish. The rigorous convergence analysis is derived based on composite energy function (CEF). In practice, the precise model of robotic fish is difficult to be obtained due to many uncertain factors. By employing ILC, the speed tracking control performance can be improved significantly without using the perfect model. Both simulations and experiments are conducted to illustrate the effectiveness of ILC, and excellent speed tracking is achieved for the robotic fish.