This paper proposes an adaptive sliding mode control based on an auxiliary distance sensor for nonlinear platoons over vehicular ad‐hoc networks under Denial‐of‐Services (DoS) attacks and external disturbances. DoS attacks can cause packet dropping for the wireless network and will lead to the collision or degradation to adaptive cruise control of platoons. To solve this problem, an adaptive Radial Basis Function (RBF) sliding mode control method is proposed for the attacked platoons. RBF neural networks are used to approximate the unknown nonlinear part and external disturbances contained in the vehicle models. Then the adaptive sliding mode controllers are designed to achieve the control goals: the spacing error converges to zero, and the platoon maintains the string stability. The system's internal stability and string stability are proved. Numerical examples are applied to show the effectiveness of the proposed method. Compared with the existing method, the spacing error is decreased from 5.0726, 6.4637 to 0.0264 for secure distributed adaptive platooning control in PF topology and LPF topology, and the average distance is increased from 7.0580 m, 5.7442 m to 11.8498 m for secure distributed adaptive platooning control in PF topology and LPF topology to maintain a safe distance under the situation of DoS attacks.
Due to the increasing demand for autonomous driving, cyber-attacks detection nowadays receives significant attention. Among several different types of attacks, Denial-of-Service (DoS) attacks can consume resources and cause the system under attack to stop responding, which can be considered a time delay model affecting the response of the attacked system. To detect the possible DoS attacks in Cooperative Adaptive Cruise Control platoon model, an adaptive observer design is proposed in this paper, where the time delay caused by DoS attacks can be fast and accurately estimated. To highlight the performance of the proposed adaptive observer, numerical simulations are applied and the performance is compared against the sliding mode observer in terms of the DoS attack time delay estimation convergence and accuracy.
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