Event-Triggered Adaptive Neural Network Trajectory Tracking Control For Underactuated Ships Under Uncertain Disturbance

Abstract An adaptive neural network (NN) event-triggered trajectory tracking control scheme based on finite time convergence is proposed to address the problem of trajectory tracking control of underdriven surface ships. In this scheme, both NNs and minimum learning parameters (MLPS) are applied. The internal and external uncertainties are approximated by NNs. To reduce the computational complexity, MLPs are used in the proposed controller. An event-triggered technique is then incorporated into the control design to synthesise an adaptive NN-based event-triggered controller with finite-time convergence. Lyapunov theory is applied to prove that all signals are bounded in the tracking system of underactuated vessels, and to show that Zeno behavior can be avoided. The validity of this control scheme is determined based on simulation results, and comparisons with some alternative schemes are presented.