Robust Adaptive Tracking Control of the Underwater Robot with Input Nonlinearity Using Neural Networks

In this paper, robust adaptive tracking control is proposed for the underwater robot in the presence of parametric uncertainties and unknown external disturbances. Backstepping control of the system dynamics is introduced to develop full state feedback tracking control. Using parameter adaptation, backstepping control and variable structure based techniques, the robust adaptive tracking control is presented for underwater robots to handle the uncertainties, saturation and dead-zone characteristics of actuators. Actuator nonlinearities comprising of dead-zone and saturation are explicitly considered in the tracking control design. Under the proposed tracking control, semi-global uniform boundedness of the closed-loop signals is guaranteed via Lyapunov analysis. Numerical simulation results are given to illustrate the effectiveness of the proposed robust adaptive tracking control.