Robust adaptive neural trajectory tracking control of surface vessels under input and output constraints

In this paper, a novel robust adaptive control scheme is developed for the trajectory tracking of surface vessels in the presence of dynamic uncertainties, unknown time-varying disturbances and input and output constraints. Firstly, the output constraint problem is transformed into the constraint problem of trajectory tracking error by coordinate transformations. Then, a nonlinear transformation is introduced to transform the tracking error into the transformed variable, with the aid of which the output constraint problem of surface vessel trajectory tracking is transformed into the boundedness problem of transformed variable, such that the various types of output constraint boundaries including constant, time-varying, symmetry and asymmetry ones can be handled in a unified framework. An auxiliary dynamic system (ADS) is applied to handle the input saturation effect. Incorporating the nonlinear transformation, the radial basis function neural networks and the ADS into dynamic surface control technique, a novel robust adaptive neural trajectory tracking control law is designed. It is theoretically proved that all signals in the closed-loop trajectory tracking control system of surface vessels are locally uniformly ultimately bounded and the actual positions and heading of surface vessels are guaranteed to lie in the constrained region. Simulation results on a scale model vessel illustrate the effectiveness of the proposed control scheme.