Coordinated Control of CAVs for Platooning Under a Parallel Distributed Model Predictive Control Framework

This paper studies cooperative control strategy design of connected and automated vehicles (CAVs) subject to coupled safety inter-vehicle distance constraints based on the distributed model predictive control (DMPC) framework to handle the vehicle dynamics constraints explicitly. Due to the limited computational capability of the vehicle control unit (VCU), it is essential to allocate the overall computational burden of the optimization problem to each vehicle. Therefore, in this work, a parallel DMPC approach is proposed to solve the overall optimal control problem in a distributed manner. To be specific, by virtue of the Lagrangian multiplier method and the dual decomposition technique, the formulated DMPC problem is first recast into a distributed dual variable consensus optimization problem comprised of a series of subsystems with local copies of the dual variables. Subsequently, the consensus optimization problem is cooperatively solved in parallel based on the alternating direction method of multiplier (ADMM). Next, the cooperative control protocols are developed under a distributed message passing mechanism to regulate the cooperative operations of CAVs.