Cooperative adaptive cruise control (CACC) is one of the many advanced driver assistance systems (ADAS) that leverage communication between nearby vehicles to maintain speed while ensuring safe following distances. The current CACC algorithms are designed with the assumption that the communication channel is secure. However, the use of communication channels makes CACC susceptible to attacks, such as False data injection (FDI). This paper develops a novel secure nonlinear controller and a nonlinear observer which can estimate FDI attacks in real-time. Furthermore, this paper shows that the developed controller and FDI attack estimation techniques ensure semi-globally uniformly bounded tracking under FDI attacks, noise, and disturbances. The efficaciousness of the proposed CACC algorithm was demonstrated in simulation and through experimental implementation. During testing using both methodologies, the controller was able to maintain a safe following distance and estimate FDI attacks and noise in real-time.

Cooperative adaptive cruise control (CACC) is one of the main features of connected and autonomous vehicles (CAVs), which uses connectivity to improve the efficiency of adaptive cruise control (ACC). The addition of reliable communication systems to ACC reduces fuel consumption, maximizes road capacity, and ensures traffic safety. However, the performance, stability, and safety of CACC could be affected by the transmission of outdated data caused by communication delays. This paper proposes a Lyapunov-based nonlinear controller to mitigate the impact of time-varying delays in the communication channel of CACC. This paper uses Lyapunov–Krasovskii functionals in the stability analysis to ensure semi-global uniformly ultimately bounded tracking. The efficaciousness of the proposed CACC algorithm is demonstrated in simulation and through experimental implementation.

This paper describes fault reconstruction issue for single input linear systems with uncertainty, using adaptive fuzzy dynamic sliding mode control (AFDSMC). The adaptive fuzzy control is used to access the control signal of dynamic sliding mode control and the adaptive law is derived. In AFDSMC sliding surface is used as the basis for reconstruction the fault signals. In AFDSMC an integrator is placed in front of the system to be controlled and the augmented system (the system plus the integrator) is one dimension bigger than the actual system and then, the plant model should be completely known. To overcome on this difficulty, an optimal loop transfer recovery observer (LTRO) is used. Comparing this method to other fault reconstruction methods, the advantage of the proposed method is to have the system controlled as well as its main task i.e. fault reconstruction.

The combination of connectivity and automation allows connected and autonomous vehicles (CAVs) to operate autonomously using advanced on-board sensors while communicating with each other via vehicle-to-vehicle (V2V) technology to enhance safety, efficiency, and mobility. One of the most promising features of CAVs is cooperative adaptive cruise control (CACC). This system extends the capabilities of conventional adaptive cruise control (ACC) by facilitating the exchange of critical parameters among vehicles to enhance safety, traffic flow, and efficiency. However, increased connectivity introduces new vulnerabilities, making CACC susceptible to cyber-attacks, including false data injection (FDI) attacks, which can compromise vehicle safety. To address this challenge, we propose a secure observer-based control design leveraging Lyapunov stability analysis, which is capable of mitigating the adverse impact of FDI attacks and ensuring system safety. This approach uniquely addresses system security without relying on a known lead vehicle model. The developed approach is validated through simulation results, demonstrating its effectiveness.

As electric vehicles (EVs) are increasingly adopted as platforms for connected and automated vehicles (CAVs), enhancing their energy efficiency becomes critical. With the emergence of vehicle-to-vehicle (V2V) communication, cooperative adaptive cruise control (CACC) offers improved traffic flow, safety, and energy efficiency by enabling real-time coordination among EVs. However, conventional CACC algorithms neglected acceleration and regenerative braking dynamics in their implementation. To address this gap, this paper proposes a third-order dynamic model for EVs which has been derived from real-world experimental data. We also propose a novel, practical, and energy-efficient Lyapunov-based CACC controller explicitly designed for EV platoons. The proposed controller is requiring lower control gains while ensuring string stability and energy efficiency. To validate its effectiveness, we conduct both simulation and experimental environments, demonstrating that our approach reduces velocity fluctuations, maintains string stability at lower headway times, and improves energy efficiency of the CACC platoon by up to 38.5% compared to a baseline CACC.