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.