Robust Data-Driven Predictive Control for Mixed Platoons under Noise and Attacks

• DOI: 10.48550/arxiv.2411.13924
• Publication Date: 2024-11-21
• Authors: Shuai Li; Chaoyi Chen; Haotian Zheng; Jiawei Wang; Qing Xu; Jianqiang Wang; Keqiang Li
• Keywords: Model Predictive Control

Controlling mixed platoons, which consist of both connected and automated vehicles (CAVs) human-driven (HDVs), poses significant challenges due to the uncertain unknown human driving behaviors. Data-driven control methods offer promising solutions by leveraging available trajectory data, but their performance can be compromised process noise adversarial attacks. To address this issue, paper proposes a Robust Data-EnablEd Predictive Leading Cruise Control (RDeeP-LCC) framework based on data-driven reachability analysis. The over-approximates system dynamics under attack using matrix zonotope set derived from develops stabilizing feedback law. By decoupling platoon into nominal error components, we employ sets recursively compute reachable that account for attacks, obtain tightened safety constraints system. This leads robust predictive framework, solved in tube-based manner. Numerical simulations human-in-the-loop experiments validate RDeeP-LCC method significantly enhances robustness improving traffic stability against practical