Laguerre-Based Distributed MPC for Cooperative Control of Connected Vehicle Platoons on Curved Roads

Vehicle platooning offers significant advantages in improving traffic efficiency and reducing energy consumption. While achieving stable tracking control is relatively straightforward on straight roads, ensuring platoon stability, energy efficiency, and computational efficiency on curved roads presents a substantial challenge due to the geometric constraints imposed by road curvature. To address this issue, this paper proposes a improved distributed model predictive control method designed for vehicle platooning on curved roads. The method combines model predictive control with Laguerre functions to enhance platooning stability on curved roads while alleviating the computational burden. To optimize energy efficiency, the vehicle’s energy consumption is integrated into the objective function. Additionally, deviations in control variables are penalized within the objective function to mitigate abrupt control actions. This, in turn, restricts rapid changes in vehicle acceleration, leading to improved driving comfort. Additionally, theoretical analysis confirms the asymptotic convergence of the proposed control system. Simulation results conducted under continuously varying speed and double-lane change scenarios demonstrate the effectiveness of the proposed method in tracking curved roads. Compared to the baseline control method, the proposed approach achieves superior tracking stability, improved energy efficiency, and reduced computational time. Notably, the proposed approach results in a 44.5% enhancement in tracking stability, a 26.8% improvement in energy efficiency, and a 9.8% reduction in computational cost under the PLF topology, highlighting its effectiveness on curved roads.