Abstract
AbstractDisturbance 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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.