Nonlinear gain-based event-triggered tracking control of a marine surface vessel with output constraints

This paper investigates the tracking control problem of marine surface vessels (MSVs) subject to time-varying output constraints, model uncertainties and unknown external disturbances. Firstly, a nonlinear gain technique is applied to the control design so that the control gain self-regulates with changes in tracking errors. Then, in the case of introducing nonlinear gain, a nonlinear gain-based barrier Lyapunov function (NGBLF) is proposed to guarantee the output tracking errors within predefined time-varying constraints. Subsequently, by employing adaptive neural networks (NNs) to approximate complex uncertainties, a nonlinear gain-based adaptive NN tracking control scheme is developed through the backstepping design tool. Based on this, in order to reduce the update frequency of controllers and mechanical wear of actuators more effectively, while ensuring the control performance, a novel dynamic event-triggered mechanism is incorporated into the control design to tune the thresholds dynamically. Afterwards, the stability analysis indicates that the presented control scheme can guarantee that all signals in the closed-loop system are semi-globally uniformly ultimately bounded and the prescribed time-varying constraints on the tracking errors will not be violated, while the Zeno behavior can be avoided. Finally, the effectiveness of the scheme is verified by simulation results.