Event-Triggered Adaptive Robust Tracking Control for Underactuated Mobile Robots

Underactuated mobile robot (UMR) system is a typical nonlinear system with uncertainty, and it can handle tasks with diverse requirements in complex working conditions. Therefore, the stability and performance of the system are of great concern. Additionally, with network traffic demands consistently outpacing advancements in network bandwidth, it is imperative to develop control algorithms that efficiently utilize the limited communication capacity. In this regard, the event-triggered mechanism (ETM) has emerged as a significant paradigm shift from traditional time-triggered mechanisms, offering a trade-off between desired control/estimation performance and enhanced resource efficiency. This study investigates the trajectory tracking control problem of UMRs in the presence of an event-triggered mechanism (ETM). The uncertainty in the system is characterized as possibly fast time-varying, bounded but with unknown bounds, and includes mismatched portions. To address these challenges, an adaptive robust constraint following control strategy is proposed, which is specifically designed to achieve accurate trajectory tracking in the presence of complex uncertainties. Furthermore, the proposed approach introduces a static-threshold event-triggered sampling strategy, which effectively balances control performance and resource utilization. The robustness and effectiveness of the proposed methodology are thoroughly established via rigorous theoretical analysis and substantiated through comprehensive simulation results.