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

Complexity 2020:1-12 (2020)
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This article investigates an adaptive neural network control algorithm for marine surface vessels with time-varying output constraints and unknown external disturbances. The nonlinear state-dependent transformation is introduced to eliminate the feasibility conditions of virtual controller. Moreover, the barrier Lyapunov function 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. Finally, the simulation results verify the benefit of the proposed method.



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