Practical Prescribed Performance Tracking Control and Optimization for Nonlinear Vehicular Platoon

This study investigates the online optimization control problem for a vehicular platoon in the presence of unmodeled vehicle dynamics, unknown external disturbances, and uncertain inter-vehicle communication topology. A novel practical performance-prescribed reinforcement learning-based distributed sliding mode (PRLDSM) control framework is constructed for the platoon to strengthen the robustness and possess the online self-learning capacity for optimizing and control. Specifically, a composite controller is proposed for the platoon which consists of an online optimal controller and a sliding mode controller. By the strong learning capacity provided by the PRLDSM controller, the optimal policy and cost function can be recursively approximated by online simultaneous tuning of both actor and critic neural networks. Moreover, it is proved that all the signals in the closed-loop platoon control system are uniformly ultimately bounded and approach zero by selecting appropriate parameters. Theoretical analysis eventually guarantees that the tracking errors can be stabilized to satisfy both the individual vehicle stability and disturbance string stability with the prescribed transient response and steady-state accuracy. The obvious feature of the proposed PRLDSM controller is that the requirement of the exact vehicle dynamics and topological matrix of the inter-vehicle communication topology can be avoided. Both numerical examples and experimental studies illustrate the effectiveness of the proposed control method.