A Distributionally Robust Optimization Model for Vehicle Platooning Under Stochastic Disturbances
Peiyu Zhang, Daxin Tian, Jianshan Zhou, Xuting Duan, Dezong Zhao, Dongpu Cao
Abstract
Inspired by connected and autonomous driving technologies, this paper proposes a closed-loop Distributionally Robust Model Predictive Control (DRMPC) method to address the problem of longitudinal platoon control disturbed by V2V communication noise. In particular, a Model Predictive Control (MPC)-based vehicle platoon control model subject to stochastic disturbances is first developed. Vehicle control and state are imposed with probabilistic chance constraints, and a state feedback structure is designed to ensure the stability of the platoon system, which poses a significant challenge to the platoon control system. To solve this computationally intractable DRMPC model, a Ball ambiguity set is constructed using the characteristic information (expectation and variance) of random variables. The original DRMPC model is reformulated into a computationally tractable robust counterpart approximation framework. Furthermore, the recursive feasibility of the proposed DRMPC and the string stability of the platoon vehicles are demonstrated by introducing an initialization strategy for nominal states. Finally, a simulation study in a platooning system consisting of six vehicles is performed to verify the validity of the DRMPC model under stochastic V2V noise disturbances.