Adaptive super‐twisting control of underwater intervention system considering dynamic couplings and uncertainties

Abstract

Abstract Underwater vehicle‐manipulator system (UVMS) is an emerging advanced equipment in underwater intervention scenarios. Several challenges limiting the performance of UVMS's manipulator are treated as burning issues, such as dynamic coupling effects, system uncertainties, disturbances etc. In this article, a novel lightly computational adaptive neural network based backstepping super‐twisting sliding mode control framework is proposed for underwater manipulator trajectory control in UVMS intervention scenario. First, a backstepping super‐twisting sliding mode control (BSSTSMC) scheme is derived, in which the chattering phenomenon of traditional sliding mode control is reduced effectively. Compared with other chattering‐free methods, the higher order control theory maintains the system robustness as well as control accuracy better. Second, a modified neural network (NN) is introduced to predict the unknown system dynamics with the consideration of coupling effects in real‐time. Then, a dimension compression strategy (DCS) for the NN's input layer is proposed to reduce the computational burden and improve the predict performance. Next, based on the Lyapunov method, the system stability is proved, in which the related error variables are guaranteed to be uniformly bounded. Finally, numerical simulations demonstrate the effectiveness and advantages of the proposed DCS‐NN‐BSSTSMC framework through the comparison with other controllers.