Adaptive dynamic surface control for trajectory tracking of autonomous surface vehicles with input and output constraints

This study aims to address the trajectory tracking control issue of autonomous surface vehicles (ASV) subjected to a constrained input and output. Firstly, combined with the tan-type Barrier Lyapunov Function, the ASV output-constrained controller is designed by an adaptive dynamic surface control technique. Secondly, the constraints on the rudder angle and the shaft speed are determined by introducing the estimation of the intermediate variable and connecting it to the Gaussian error function. Finally, a radial basis function neural network approximates the unknown disturbance's complex part, and Lyapunov stability analysis is used to verify that the proposed control scheme, ensures the error remains within the predefined boundary and that all signals in the system are uniformly and ultimately bounded. The results show that compared with the comparison control method, the proposed scheme's design can suppress the vibration of the state parameter signals and resist unknown interference. This study can restrict the amplitude and frequency of the rudder and the shaft speed to improve the safety of navigation and save energy.