Adaptive Neural Network Control of a Fully Actuated Marine Surface Vessel with Multiple Output Constraints

In this brief, we investigate the control problem of tracking a desired trajectory for a fully actuated marine surface vessel considering multiple outputs constraints. To prevent multiple output constraints violation, a symmetric barrier Lyapunov function (SBLF) is employed. Backstepping, in combination with adaptive feedback approximation techniques, is introduced to design an adaptive neural network control. Experimental simulations are provided to evaluate the feasibility and effectiveness of the proposed controller. Compared to the adaptive neural network control without multiple output constraints, the proposed adaptive neural network using the SBLF can guarantee that all the outputs remain bounded.