Multi-Parametric model predictive control for vehicle platoons

Traffic congestion has emerged as a pressing issue in today’s transportation systems, resulting in significant energy consumption and time wastage. One potential solution to alleviate traffic jams is the utilization of connected automated vehicles (CAVs) in the form of vehicle platoons. In such platoons, the lead vehicle sends its essential information to others through the dedicated short-range communication system (DSRC), enabling the followers to maintain a safe distance and velocity. Model predictive control (MPC) is widely recognized as an effective control strategy for regulating platoon behavior. However, its online optimization process involves complex calculations, resulting in sluggish performance. This study aims to design a more efficient control strategy for vehicle platoons and to enhance their overall performance through explicit model predictive control (eMPC). By solving the optimization problem offline in a multiparametric manner, the proposed approach eliminates the computational overhead associated with implicit MPC, making it well-suited for high-speed systems like automotive applications. Simulation results demonstrate that the proposed controller achieves high accuracy in tracking the reference input and assumed acceleration profile, ensuring the stable behavior of the vehicle platoon throughout the pre-defined scenario. Furthermore, the controller is implemented in Prescan software using actual acceleration data for the lead vehicle, confirming its effectiveness under more realistic conditions. Evaluation of the results reveals that the designed controller significantly improves the platoon’s performance in terms of tracking the behavior of the lead vehicle, as measured by the mean squared error (MSE) metric.