Adaptive Cruise Control Algorithm for Persistence and Security of Distributed Heterogeneous Vehicle Platoon Based on Discrete-Time Control Barrier Function

This paper addresses the persistence and security problem of nonlinear distributed heterogeneous platoons during driving based on the discrete-time control barrier function (DCBF). Introducing a set of DCBF constraints in nonlinear distributed model predictive control (NDMPC) encodes persistent and safe behaviors as forward invariance of sets in the vehicle state space, enabling it to constrain the workshop distance and their electricity during driving simultaneously. To address the uncertainty of waiting time for vehicles at charging stations, the Markov decision process is introduced to optimize the charging strategy on the basis of ensuring the persistence, thereby reducing the time for vehicles to return to the end of the queue under speed limit conditions. By combining a set of constructed DCBFs with Markov decision processes and incorporating them into NDMPC, the dynamic behavior of heterogeneous platoon driving processes can be optimized. The vehicle will make a decision after each visit to a charging station, which can not only ensure that the vehicle reaches a charging station with shorter waiting time in queues before running out of battery but also ensure that the spacing between any two successive vehicles in the platoon is not less than the expected safe distance. The proposed improved NDMPC-DBCBF algorithm can ensure the persistence of all vehicles in the heterogeneous platoon during driving, improve the safety of vehicle following, and effectively solve the challenges brought by the uncertainty of waiting time at charging stations. Finally, an experimental simulation validates the feasibility of the suggested control algorithm.