Tracking Control With Input Saturation and Full-State Constraints for Surface Vessels

To solve the problems of full-state constraints in trajectory tracking of surface vessels, a backstepping technique combining a novel integral barrier Lyapunov function (iBLF) with neural network and sliding mode is proposed. Moreover, the control law is extended to the control problem with input saturation. First, the iBLF-based control approach is applied to the control design. The purpose of the iBLF-based approach is to deal with the constraints without transforming the constraints bound into the tracking errors bound. Second, the Neural Networks (NN) is used to handle with the system uncertainties, and a single parameter online adjustment is used instead of the weights online adjustment of the neural networks to realize the adaptive estimation of a single parameter. Third, defining an auxiliary analysis system to deal with the effect of input saturation on the system, an effective control approach under input saturation is realized. Furthermore, it is proved that the designed control law can guarantee the uniformly ultimately bounded stability of closed-loop system and system state can not violate the constraints. Finally, the simulation results of trajectory tracking control of the surface vessel show that the proposed control approach can effectively solve the control problem of nonlinear systems with full-state constraints, system uncertainties and input saturation.