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

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.