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Vehicle platoon is one of the innovations in the automated highway systems, which has the potential to reduce fuel consumption, alleviate traffic congestion and lighten the driver’s burden. How to control the vehicle effectively to ensure the stability of the queue is a challenge. Aiming to overcome the shortcomings of the platoon control method based on traditional sliding mode control, a non-singular terminal sliding mode control method optimized by the extreme learning machine is proposed in this paper. Firstly, the vehicle longitudinal dynamics are derived from the analysis of the forces acting on the vehicle in the longitudinal direction. A constant time headway policy is taken as the spacing policy. The modified non-singular terminal sliding mode control method has outstanding performance, simulation results demonstrate that the following vehicles can rapidly track the trajectory of the leading vehicle in the platoon with less spacing error and guarantee string stability. In this study, several experiments are set up to consider the disturbance and other uncertain practical factors. The performance of the proposed method is superior to the traditional sliding mode control method. Experimental results show that the proposed method can significantly reduce chattering and has good robustness under the circumstances of the disturbance.

ELM-Based Non-Singular Fast Terminal Sliding Mode Control Strategy for Vehicle Platoon