Distributed model predictive control for heterogeneous vehicle platoon with unknown input of leading vehicle

Abstract

In this paper, a distributed model predictive control (DMPC) algorithm for a platoon of heterogeneous vehicles is proposed. The leading vehicle is allowed to be driven by a non-zero and time-varying input, rather than traveling at a constant velocity. Except for individual state and input constraints for each vehicle, all vehicles are coupled via state-coupled inter-vehicular spacing constraints and state-coupled cost functions, which maintain the unidimensional platoon formation with satisfactory transient performance. Each vehicle communicates with its neighboring vehicles, and may not know the leading vehicle’s kinetic status information. The control input of each following vehicle is computed by a local optimization problem established by each vehicle’s local information and the assumed state information from its neighbors. By designing distributed terminal control laws for following vehicles, dividing each state-coupled set into several specific subsets, and then forcing each following vehicle to optimize its state constrained in the assigned subsets, the coupled constraints and cost functions can be decoupled, and thus a distributed and parallel computing method can be adopted to compute the control inputs of all following vehicles. Based on the tailored terminal equality constraints together with the tailored terminal control laws, the recursive feasibility of local MPC optimization problems is achieved at all time steps and the asymptotic stability of each vehicle is also guaranteed. The effectiveness of the proposed DMPC method is demonstrated in simulation, and the advantage of the proposed DMPC dealing with the leading vehicle’s non-zero, inaccessible, and time-varying input is highlighted by a comparison simulation for heterogeneous vehicle platoon with a continuously changing leading vehicle velocity.