An efficient neural network approach to tracking control of an autonomous surface vehicle with unknown dynamics

This paper proposes an efficient neural network (NN) approach to tracking control of an autonomous surface vehicle (ASV) with completely unknown vehicle dynamics and subject to significant uncertainties. The proposed NN has a single-layer structure by utilising the vehicle regressor dynamics that expresses the highly nonlinear dynamics in terms of the known and unknown dynamic parameters. The learning algorithm of the NN is simple yet computationally efficient. It is derived from Lyapunov stability analysis, which guarantees that all the error signals in the control system are uniformly ultimately bounded (UUB). The proposed NN approach can force the ASV to track the desired trajectory with good control performance through the on-line learning of the NN without any off-line learning procedures. In addition, the proposed controller is capable of compensating bounded unknown disturbances. The effectiveness and efficiency are demonstrated by simulation and comparison studies.