摘要
Disturbance observer-based control method has achieved good results in the carfollowing scenario of intelligent and connected vehicle(ICV).However,the gain of conventional extended disturbance observer(EDO)-based control method is usually set manually rather than adjusted adaptively according to real time traffic conditions,thus declining the car-following performance.To solve this problem,a car-following strategy of ICV using EDO adjusted by reinforcement learning is proposed.Different from the conventional method,the gain of proposed strategy can be adjusted by reinforcement learning to improve its estimation accuracy.Since the“equivalent disturbance”can be compensated by EDO to a great extent,the disturbance rejection ability of the carfollowing method will be improved significantly.Both Lyapunov approach and numerical simulations are carried out to verify the effectiveness of the proposed method.
基金
State Key Laboratory of Automotive Safety and Energy,Grant/Award Number:KFY2208
National Natural Science Foundation of China,Grant/Award Numbers:U2013601,U20A20225
Key Research and Development Plan of Anhui Province,Grant/Award Number:202004a05020058
the Natural Science Foundation of Hefei,China(Grant No.2021032)。