Backstepping neurodynamics based position-tracking control of underactuated autonomous surface vehicles

Backstepping is a very effective methodology for tracking control of underactuated autonomous surface vehicles (ASVs), but the fundamental drawback is that it requires to calculate numerical derivatives of virtual velocity control signals. In this paper, we propose a novel bioinspired neurodynamics based approach by integrating the backstepping technique and three shunting neural dynamics models. Instead of differentiating the virtual control signal directly at each step of the backstepping design procedure, we let the corresponding virtual signal pass through a shunting neuron model to avoid the complexity of differentiation. The proposed tracking control algorithm is thus much simpler than that constructed based on the conventional backstepping approach. The stability of the control system is proved that all signals are uniformly ultimately bounded (UUB), and the position-tracking error converges to a small neighborhood of the origin. Simulations demonstrate the effectiveness of the proposed approach.