Evolutionary‐games approach for distributed predictive control involving resource allocation

Abstract

This paper proposes a distributed model predictive control (DMPC) scheme based on population games for a system formed by a set of subsystems. In addition to considering independent operational constraints for each subsystem , the controller addresses a coupled constraint that involves the sum of all control inputs. This constraint models an upper bound on the total amount of energy supplied to the plant. The proposed approach does not need a centralized coordinator when having a coupled constraint involving all the decision variables. The proposed methodology, which takes advantage of evolutionary game theory concepts, provides an optimal solution for the described problem. Moreover, it is shown that the methodology has plug-and-play features, i.e., for each already designed local MPC controller nothing changes when more subsystems are added/removed to/from the global constrained control problem. Furthermore, the stability analysis of the proposed DMPC scheme is presented.

Online Network-Constrained Dispatch of Distributed Generators in Radial Networks

“…This is a scenario where replicator dynamics will not converge. Otherwise, distributed population dynamics can achieve the same results obtained by centralized dynamics as shown in previous literature [5], [7], [8], [14]. This work extends the work previously done in [8] by including network constraints to a distributed smith dynamics approach.…”

Online Network-Constrained Dispatch of Distributed Generators in Radial Networks

“…Otherwise, distributed population dynamics can achieve the same results obtained by centralized dynamics as shown in previous literature [5], [7], [8], [14]. This work extends the work previously done in [8] by including network constraints to a distributed smith dynamics approach.…”

Gain-Scheduled Mean-Field-Type Control for a Non-Linear Continuous Stirred Tank Reactor

“…Here, we use the non-linear stochastic model. For instance, in [28], a distributed model predictive is designed for a linear CSTR. In [29], a nonlinear adaptive control is designed by using Fourier integral for a CSTR.…”