Motion control of a robotic fish via learning control approach with self-adaption

In this paper, a novel work is presented, where a learning-based control approach is proposed for motion control for a two-link robotic fish. First, by virtue of the Lagrangian mechanics method, we establish a mathematical model for the two-link Carangiform robotic fish. According to the constructed dynamical model, P-type learning control laws are proposed for speed and turning control of the robotic fish. Furthermore, due to the complexity of the dynamical model of the robotic fish, a self-adaption rule is introduced for learning gains, which might expedite the convergence rate of learning. In the end, the efficiency of the proposed learning controllers are illustrated by simulations.