Reference
R. Luo, R. Bourdais,
T. J. J. van
den Boom, and B. De Schutter, "Multi-agent model predictive control based on
resource allocation coordination for a class of hybrid systems with limited
information sharing,"
Engineering Applications of Artificial
Intelligence, vol. 58, pp. 123-133, Feb. 2017.
Abstract
We develop a multi-agent model predictive control method for a class of hybrid
systems governed by discrete inputs and subject to global hard constraints. We
assume that for each subsystem the local objective function is convex and the
local constraint function is strictly increasing with respect to the local
control variable. The proposed multi-agent control method is based on a
distributed resource allocation coordination algorithm and it only requires
limited information sharing among the local agents of the subsystems. Thanks to
primal decomposition of the global constraints, the distributed algorithm can
always guarantee global feasibility of the local control decisions, even in the
case of premature termination. Moreover, since the control variables are
discrete, a mechanism is developed to branch the overall solution space based
on the outcome of the resource allocation coordination algorithm at each node
of the search tree. Finally, the proposed multi-agent control method is applied
to the charging control problem of electric vehicles under constrained grid
conditions. This case study highlights the effectiveness of the proposed
method.
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BibTeX
@article{LuoBou:16-025,
author = {Luo, Renshi and Bourdais, Romain and van den Boom, Ton J. J. and
De Schutter, Bart},
title = {Multi-Agent Model Predictive Control Based on Resource Allocation
Coordination for a Class of Hybrid Systems with Limited
Information Sharing},
journal = {Engineering Applications of Artificial Intelligence},
volume = {58},
pages = {123--133},
month = feb,
year = {2017}
}