Resilient Event Triggered Interval Type‐2 Fuzzy Sliding Mode Control for Connected and Autonomous Vehicles Subjected to Multiple Cyber Attacks

Abstract

Connected and autonomous vehicles (CAVs) are considered a hot area of research in the field of intelligent transportation systems. However, over the past few years, cybersecurity threats have posed significant challenges to such systems, given the ever‐evolving automotive industry. Thus, there is a growing need to design resilient control strategies to address the issue of cyberattacks. This article proposes the design of a distributed multiagent expert control scheme for cyberattack‐resilient control of CAVs. The study implemented an event‐triggered consensus‐based attack detection scheme capable of distinguishing between replay (RA), denial‐of‐service (DoS), and false data injection (FDI) attacks. The attacks in this study occur randomly, are bounded and time‐varying, and can overlap with each other. It was demonstrated that by considering an estimator error bound ε for the attacked signal reconstruction, the SMC controller in feedback with a vehicle remains stable in the sense of Lyapunov. Conditions were then provided that guarantee global asymptotic stability for a minimum dwell‐time constraint , and the platoon was shown to be string stable for the minimum distance between vehicles, denoted as . Finally, the performance of the control strategy was evaluated using multiple performance indices, considering platoons of varying sizes.