Neural-network-based Self-triggered Observed Platoon Control for Autonomous Vehicles

TL;DR

This paper presents an adaptive control framework for autonomous vehicle platoons that improves robustness and communication efficiency using neural networks, observers, and self-triggered mechanisms under uncertain dynamics.

Contribution

It introduces a novel integration of neural networks, distributed observers, and self-triggered control for nonlinear multi-agent systems in vehicle platooning.

Findings

Achieves high robustness and adaptability in simulations.

Reduces communication load with self-triggered mechanism.

Guarantees bounded tracking errors and avoids Zeno behavior.

Abstract

This paper investigates autonomous vehicle (AV) platoon control under uncertain dynamics and intermittent communication, which remains a critical challenge in intelligent transportation systems. To address these issues, this paper proposes an adaptive consensus tracking control framework for nonlinear multi-agent systems (MASs). The proposed approach integrates backstepping design, a nonlinear sampled-data observer, radial basis function neural networks, and a self-triggered communication mechanism. The radial basis function neural networks approximate unknown nonlinearities and time-varying disturbances, thereby enhancing system robustness. A distributed observer estimates neighboring states based on limited and intermittent measurements, thereby reducing dependence on continuous communication. Moreover, self-triggered mechanism is developed to determine triggering instants, guaranteeing a strictly positive minimum inter-event time and preventing Zeno behavior. The theoretical analysis proves that all closed-loop signals are uniformly ultimately bounded (UUB), and tracking errors converge to a compact set. Simulation results demonstrate that the proposed approach achieves high robustness, adaptability, and communication efficiency, making it suitable for real-world networked vehicle systems.