Adaptive neural network trajectory tracking control for underactuated unmanned surface vehicle

Abstract:

To deal with the problems of horizontal trajectory tracking control of underactuated unmanned surface vehicle (USV) in the presence of model uncertainties and unknown ocean currents, a robust nonlinear trajectory tracking controller for underactuated USV was proposed based on the backstepping method and adaptive technique. For the model uncertainties of underactuated USV, the adaptive neural network technique was employed to estimate and compensate the unknown model uncertainties. Then, the derivatives of virtual control variables were obtained by dynamic surface control method. The control law was simple in structure and easy to be realized in engineering, and it greatly reduced the complexities of the traditional backstepping method. For the disturbances of unknown time-varying currents, an observer was designed to estimate the velocity of unknown time-varying currents. Next, based on Lyapunov’s direct method, it was proved that the designed controller could ensure that the motion trajectory converged to the expected value, and that all signals of the trajectory tracking closed-loop control system were finally uniformly bounded. Lastly, under the condition of limited control input, in order to verify the tracking performance of the controller, the circular trajectory was selected as the reference trajectory. Simulations were carried out and results show that the controller could accurately track the desired trajectory and had strong robustness for model uncertainties and unknown time-varying currents. In addition, the unknown functions of the system were effectively estimated and compensated by adaptive neural network technique, which verified the effectiveness of the proposed tracking control scheme.