A Holistic Robust Motion Control Framework for Autonomous Platooning
Hong Wang, Liming Peng, Zichun Wei, Kai Yang, Xian–Xu Bai, Luo J, Ehsan Hashemi
Abstract
Safety is the foremost concern for autonomous platooning. The vehicle-to-vehicle (V2V) communication delays and the sudden appearance of obstacles will trigger the safety of the intended functionality (SOTIF) issues for autonomous platooning. This research proposes a holistic robust motion controller framework (MCF) for an intelligent and connected vehicle platoon system. The MCF utilizes a hierarchical structure to resolve the longitudinal string stability and the lateral control problem under the complex driving environment and time-varying communication delays. Firstly, the H-infinity feedback controller is developed to ensure the robustness of the platoon under time-varying communication delay in the upper-level coordination layer (UCL). The output from UCL will be delivered to the lower-level motion-planning layer (LML) as reference signals. Secondly, the model predictive control (MPC) algorithm is implemented in the LML to achieve multi-objective control, which comprehensively considers the reference signals, the artificial potential field, and multiple vehicle dynamics constraints. Furthermore, three critical scenarios are co-simulated for case studies, including platooning under time-varying communication delay, merging, and obstacle avoidance scenarios. The simulation results indicate that, compared with single-structure MPC, the proposed MCF can offer a better suppression on position error propagation, and get improvements on maximum position error in the three scenarios by 19.2%, 59.8%, and 15.3%, respectively. Lastly, the practicability and effectiveness of the proposed MCF are verified via the hardware-in-the-loop experiment. The average conducting time of the proposed method on the Speedgoat real-time target machine is 1.1 milliseconds, which meets the real-time requirements.