Switching Dynamic Event-Triggered Sliding Mode Based Trajectory Tracking Control for ASVs With Nonlinear Dead-Zone and Saturation Inputs

Abstract

This paper investigates discrete-time sliding mode trajectory tracking control for fully actuated autonomous surface vessels (ASVs) with unknown nonlinear dead-zone and saturation inputs, utilizing a switching dynamic event-triggered mechanism (DETM). Through model integration, a direct relationship between ASV position and control inputs is established, simplifying trajectory tracking strategy design. ASVs face dead-zone and saturation constraints in control inputs, where low input signals may not overcome static friction, hindering maneuverability, and further increases are ineffective once actuators reach maximum thrust. Unlike linear dead-zone and saturation input constraints with known parameters, this paper considers a more realistic scenario of unknown nonlinearity, employing adaptive neural networks to approximate and compensate for the resulting unknown dynamics. Moreover, limited internal communication resources constrain real-time inter-subsystem communication in ASVs, while frequent short-period sampling in stable conditions results in unnecessary energy and computational consumption, collectively degrading trajectory tracking performance. A novel switching DETM is proposed to reduce unnecessary data transmission, which switches triggering conditions based on variations in auxiliary dynamic variables. Meanwhile, the controller output variation is integrated into the event-triggered conditions to enhance tracking control performance. Based on this, a discrete-time sliding mode trajectory tracking controller suitable for large sampling periods is designed. This ensures satisfactory tracking control effectiveness while further reducing unnecessary data transmission frequency and conserving limited communication resources within a larger range of sampling periods. All tracking errors are proven to be controlled within a small vicinity near zero. The numerical simulation results validate the efficacy of the proposed control strategy.