Adaptive trajectory tracking control of underwater salvage vehicle based on full-state constraint

Aiming at the trajectory tracking control problem of underwater salvage vehicle affected by dynamic uncertainty and external disturbance, a trajectory tracking control scheme based on full-state constraint is proposed. Firstly, radial basis function neural network (RBFNN) and adaptive virtual parameter learning method are combined to compensate dynamic uncertainty and external interference. Then barrier Lyapunov function (BLF) is used to prevent the violation of the full-state constraint. The Lyapunov stability theory is used to prove that the proposed control scheme can achieve semi-global uniform boundedness of the closed-loop system. Finally, simulation results further demonstrate its excellent performance. The proposed control scheme has good reference value for the application of the underwater salvage vehicle in practical engineering.