hybrid adaptive neural network auv controller design with sliding mode robust term

This work addresses an autonomous underwater vehicle (auv) for applying nonlinear control which is capable of disturbance rejection via intelligent estimation of uncertainties. adaptive radial basis function neural network (rbf nn) controller is proposed to approximate unknown nonlinear dynamics. the problem of designing an adaptive rbf nn controller was augmented with sliding mode robust term to improve trajectory tracking and regulation in presence of uncertainties. moreover, stability proof of proposed control scheme was shown with lyapunov theory. furthermore, the control, design and simulation results are provided without any simplification of the entire system. although the design approach of this paper is implemented on remus this point of view can be applied on any auv using the same technique.