Adaptive Wave Neural Network Nonsingular Terminal Sliding Mode Control for an Underwater Manipulator with Force Estimation

This paper proposes an adaptive wave neural network nonsingular terminal sliding mode control (AWNN-NTSMC) strategy with force estimation, which is exploited to address the path tracking control problem of the underwater manipulator under lumped disturbances. The proposed control scheme contains three parts: a nonsingular terminal sliding mode surface (NTSMS) part, an AWNN part and a force estimation part. The NTSMS is designed to make the system states achieve fast convergence in the sliding mode phase. The AWNN theory is utilized to approximate the lumped disturbances via on-line adjustment of the network parameters. The force estimation method is applied in compensating the effect of external force on the control system. Besides, a saturated function instead of the signum function is used aiming to the chattering suppression. Asymptotic stability of the closed-loop system is guaranteed by the Lyapunov stability. Finally, by using a six degree of freedom (DOF) underwater manipulator, comparative simulation results validate the better tracking performance and stronger robustness against disturbances of our proposed scheme.