Disturbance Observer-Based Adaptive Neural Network Control of Marine Vessel Systems with Time-Varying Output Constraints

Abstract EN

This article investigates an adaptive neural network (NN) control algorithm for marine surface vessels with time-varying output constraints and unknown external disturbances.

The nonlinear state-dependent transformation (NSDT) is introduced to eliminate the feasibility conditions of virtual controller.

Moreover, the barrier Lyapunov function (BLF) is used to achieve time-varying output constraints.

As an important approximation tool, the NN is employed to approximate uncertain and continuous functions.

Subsequently, the disturbance observer is structured to observe time-varying constraints and unknown external disturbances.

The novel strategy can guarantee that all signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB).

Finally, the simulation results verify the benefit of the proposed method.