This paper proposes an adaptive dynamic positioning (DP) control method based on a multi-observer fusion architecture with minimal sensor requirements. A sliding mode observer is designed based on a high- and low-frequency superposition model to filter high-frequency state variables, while a finite-time convergence disturbance observer estimates unknown time-varying low-frequency disturbances online. For efficient handling of model uncertainties, a single-parameter learning neural network is implemented that requires only one parameter to be estimated online. The control system employs auxiliary dynamic systems to handle input saturation constraints and considers thruster system dynamics. Theoretical analysis demonstrates the stability of the observer-fusion control strategy, while simulation results based on the SimuNPS platform validate its effectiveness in state estimation and disturbance rejection compared to traditional sensor-dependent methods.

This paper addresses the critical issue of dynamic performance constraints for unmanned surface vessels (USVs) operating under real maritime conditions. First, to accurately characterize the effect of ocean waves on unmanned vessels, a wave disturbance model is established based on actual ocean wave measurements, which provides more accurate representation of maritime environmental disturbances. Subsequently, an optimized barrier Lyapunov function is designed to constrain the position errors that occur during the USV’s trajectory tracking process. Furthermore, a state-constrained control strategy that incorporates propeller dynamics is developed, which can effectively mitigate oscillations in control inputs. Finally, numerical simulations using SimuNPS software demonstrate that the proposed control strategy exhibits significant advantages over existing methods.