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This paper introduces a new approach of treating platoon systems usingmean-variance control formulation. The underlying system is a controlledswitching diffusion in which the random switching process is a continuous-timeMarkov chain. This switching process is used to represent random environmentand other random factors that cannot be given by stochastic differentialequations driven by a Brownian motion. The state space of the Markov chain islarge in our setup, which renders practically infeasible a straightforwardimplementation of the mean-variance control strategy obtained in theliterature. By partitioning the states of the Markov chain into sub-groups (orclusters) and then aggregating the states of each cluster as a super state, weare able to obtain a limit system of much reduced complexity. The justificationof the limit system is rigorously supported by establishing certain weakconvergence results.

A Mean-Variance Control Framework for Platoon Control Problems: Weak Convergence Results and Applications on Reduction of Complexity