Event-Triggered Tube Model Predictive Control for Robust Vehicle Motion Tracking Under Uncertain Dynamics and External Disturbances

Abstract

The deployment of autonomous and semi-autonomous ground vehicles places exacting demands on motion tracking systems that must operate in uncertain environments, under changing vehicle dynamics, and in the presence of external disturbances such as crosswinds, road surface irregularities, and sensor degradation. This paper presents a system-level analysis of event-triggered tube model predictive control as an architectural framework for robust vehicle motion tracking. Rather than focusing exclusively on algorithmic formulations, the discussion addresses the coupling between predictive control structures, disturbance compensation, computational resource allocation, communication infrastructure, and safety governance. The event-triggered mechanism is examined as a means of reducing computational and communicative load while preserving robustness through tube-based uncertainty propagation. The paper further considers structural trade-offs involving prediction horizon length, triggering thresholds, state constraint tightening, and real-time solver performance. Infrastructure implications for embedded deployment, failure handling, and lifecycle sustainability are analyzed alongside cross-domain comparisons with networked control and large-scale data-driven systems. The analysis shows that event-triggered tube model predictive control offers a principled path toward balancing robust constraint satisfaction with practical resource limitations. However, its successful deployment depends on governance mechanisms, validation procedures, and lifecycle management that extend beyond the control law itself. The paper contributes a synthetic interdisciplinary perspective intended to inform both engineering design and institutional decision-making for robust autonomous vehicle control.