TY - GEN
T1 - Resilient Predictive Control of Constrained Connected and Automated Vehicles under Malicious Attacks
AU - Wei, Henglai
AU - Wang, Yan
AU - Chen, Jicheng
AU - Zhang, Hui
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, we present a novel resilient distributed model predictive control (RDMPC) framework for the con-strained Connected and Automated Vehicles (CAV) in the pres-ence of F -local malicious attacks. The proposed framework aims to ensure constraint satisfaction and identify malicious attacks using previously broadcast information and a convex set, referred to as the 'resilience set.' Compared to the well-known Mean Subsequence Reduced (MSR) algorithms that require (2F + 1)-robust graphs, the proposed approach significantly reduces the required robustness level to (F + 1)-robust graph. Our simulation results demonstrate the effectiveness of the proposed approach in mitigating the impact of malicious attacks on constrained CAVs while ensuring constraint satisfaction. Overall, the proposed RDMPC framework contributes to the field of resilient platoon control for CAVs and has potential implications for improving the reliability and security of CAVs in real-world scenarios.
AB - In this paper, we present a novel resilient distributed model predictive control (RDMPC) framework for the con-strained Connected and Automated Vehicles (CAV) in the pres-ence of F -local malicious attacks. The proposed framework aims to ensure constraint satisfaction and identify malicious attacks using previously broadcast information and a convex set, referred to as the 'resilience set.' Compared to the well-known Mean Subsequence Reduced (MSR) algorithms that require (2F + 1)-robust graphs, the proposed approach significantly reduces the required robustness level to (F + 1)-robust graph. Our simulation results demonstrate the effectiveness of the proposed approach in mitigating the impact of malicious attacks on constrained CAVs while ensuring constraint satisfaction. Overall, the proposed RDMPC framework contributes to the field of resilient platoon control for CAVs and has potential implications for improving the reliability and security of CAVs in real-world scenarios.
KW - Distributed model predictive control
KW - connected and automated vehicle
KW - malicious attacks
KW - platoon control
UR - https://www.scopus.com/pages/publications/85163084546
U2 - 10.1109/ICPS58381.2023.10128093
DO - 10.1109/ICPS58381.2023.10128093
M3 - 会议稿件
AN - SCOPUS:85163084546
T3 - Proceedings - 2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems, ICPS 2023
BT - Proceedings - 2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems, ICPS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Industrial Cyber-Physical Systems, ICPS 2023
Y2 - 8 May 2023 through 11 May 2023
ER -