Robust adaptive control for dynamic positioning vehicles in presence of adjustable threshold rule and input constraints

In this work, we present a new class of event-triggered mechanism for dynamic positioning vehicles under input saturation and system uncertainties. The class is characterized by the introduction of an internal dynamic variable, which motivated the proposed name of the dynamic event-triggered mechanism. Different from the traditional event-triggered mechanism with invariable thresholds, the proposed mechanism with the adjustable threshold parameters could reduce communication burden. The internal adjustable threshold parameters are updated online in an adaptive manner. Furthermore, an auxiliary system is employed to resolve the input saturation of actuators. System uncertainty and explosion of complexity are resolved by combining dynamic surface control technique and robust neural damping technique. Due to the virtues of the above design, the proposed algorithm is concise in form and easy to be implemented in the marine practice. Based on the Lyapunov theory, it is proved that the closed-loop system is semi-globally uniform ultimate bounded (SGUUB). Finally, the effectiveness and feasibility of the proposed algorithm are verified by simulation experiments. •A novel event-triggered law is designed with the adjustable threshold parameters.•The auxiliary system is employed to resolve the input saturation of actuators.•The robust neural damping technique is developed to guarantee the control precision.•The derived closed-loop system is semi-globally uniform ultimate bounded.