Event-Triggered Robust Path Tracking Control Considering Roll Stability Under Network-Induced Delays for Autonomous Vehicles

Fernando Viadero‐Monasterio; Nguyen, Anh‐Tu; Lauber, Jimmy; Boada, María Jesús López; Boada, Beatriz L. · 2023 · OpenAlex-citations

DOI: 10.1109/tits.2023.3321415

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Summary

This paper addresses the challenge of designing robust path tracking controllers for autonomous vehicles that simultaneously ensure lateral stability and roll stability, while accounting for network-induced delays and communication constraints. The motivation stems from the high risk of rollover accidents, particularly in vehicles with high centers of mass, and the need to balance safety with passenger comfort. Existing control strategies often treat path tracking and roll stability separately or rely on computationally intensive Model Predictive Control (MPC) methods that struggle with real-time implementation and robustness under uncertainty. Furthermore, many approaches assume full state measurability, which is impractical due to sensor costs. This work proposes a unified Multi-Input Multi-Output (MIMO) control strategy that integrates steering control for path tracking and active suspension for roll stability, utilizing a Static Output Feedback (SOF) scheme to rely only on sensors available in series-production vehicles. The methodology employs a Linear Parameter-Varying (LPV) control framework to handle the time-varying nature of vehicle speed and the asynchronous phenomena caused by sampling and network delays. The vehicle dynamics are modeled with three degrees of freedom: sideslip angle, yaw rate, and roll angle. To manage the dependency on longitudinal velocity, a change of variable is introduced to create a polytopic representation of the system. The control design uses an augmented Lyapunov-Krasovskii functional to derive stability conditions that guarantee closed-loop stability despite transmission delays and event-triggered signal updates. The design procedure is formulated as an iterative optimization problem involving Linear Matrix Inequality (LMI) constraints, solvable via semidefinite programming. An event-triggering mechanism is implemented to reduce communication burden by transmitting control signals only when the difference between the current and last transmitted signals exceeds a specific threshold, thereby avoiding Zeno behavior and network saturation. The proposed controller was validated using CarSim vehicle dynamics simulation software under various dynamic scenarios. The results demonstrate that the event-triggered SOF controller effectively maintains path tracking accuracy while significantly improving roll stability and passenger comfort compared to existing methods. The comparative study highlights the controller's ability to generate smooth control signals and maintain stability under network-induced delays. Crucially, the event-triggering mechanism successfully reduced the amount of data exchanged over the vehicle network, alleviating communication burden without compromising control performance. The approach ensures that the vehicle remains stable even when control signals are updated intermittently, addressing the limitations of continuous transmission schemes. The significance of this work lies in its contribution to the development of safe and efficient autonomous driving systems. By integrating roll stability into path tracking control within a robust LPV framework, the paper provides a solution that enhances both safety and comfort. The use of SOF control makes the method practical for real-world applications by eliminating the need for expensive sensors or complex observers. Additionally, the incorporation of event-triggered control offers a viable strategy for managing the communication constraints of networked control systems in autonomous vehicles. This approach offers a robust alternative to MPC, providing guaranteed stability and reduced computational complexity, thus advancing the state-of-the-art in vehicle control systems.

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• teleoperation remote driving