Decentralized Optimal Control for Large Populations of Two-Wheeled Vehicles
Journal
Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
Journal Volume
2017-January
Pages
3009 - 3014
Date Issued
2017
Author(s)
Abstract
In this paper, we consider decentralized optimal control for large populations of two-wheeled vehicles via mean field game theory. Specifically, the main objective is that each two-wheeled vehicle follows the average behavior (or mean field) of the whole population while achieving the overall optimal control performance without sharing their state information (position and/or velocity) with other vehicles. We first provide a general modeling framework of the two-wheeled vehicle for its position control by using balances of moments and forces that are obtained from the two electric DC motors and the vehicle chassis structure. Next, we design an optimal control for each vehicle, which is decentralized as it is a function of its own state, and the set of designed decentralized optimal controls constitutes an -Nash equilibrium. With these decentralized optimal controls, we characterize an approximated average behavior of the vehicles, and show that it is the best estimate of the actual average behavior when the population size becomes arbitrarily large. Finally, these theoretical results are validated through simulation and experiment results of large populations of two-wheeled vehicles. © 2017 IEEE.
Event(s)
43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017
Other Subjects
DC motors; Game theory; Industrial electronics; Population statistics; Position control; Vehicles; Average behavior; Large population; Mean field games; Optimal controls; Population sizes; State information; Two wheeled vehicles; Vehicle chassis; Electric machine control
Type
conference paper