Car-following strategy of intelligent connected vehicle using extended disturbance observer adjusted by reinforcement learning

Disturbance observer-based control method has achieved good results in the car-following 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 car-following method will be improved significantly. Both Lyapunov approach and numerical simulations are carried out to verify the effectiveness of the proposed method.