Adaptive Optimal Control for Vehicle Platoons With Uncertain Communication Topology

This study proposes the reinforcement learning adaptive optimal (RLAO) control scheme for vehicle platoons subject to unmodeled vehicle dynamics, unknown external disturbances and uncertain inter-vehicle communication topology. By combining the actor-critic RL algorithm with adaptive backstepping technique, a novel RLAO control framework is established for each follower to stabilize the tracking errors and optimize the control performance. The proposed RLAO controller does not depend on the exact topological matrix and vehicle dynamics, as only the input and output data from the tracking error dynamics are involved in updating the control input. Based on the optimal control and dynamic programming, a useful algorithm is proposed to implement the RLAO controller such that all the error signals of the platoon control system are ultimately uniformly bounded. Particularly, the tracking errors can converge to a small neighborhood of zero by setting appropriate parameters. Furthermore, the platoon disturbance string stability is guaranteed. A comparative analysis based on simulation examples is discussed to illustrate the effectiveness of the proposed RLAO controller.