Distributed MPC for resource‐constrained control systems

Abstract

This article presents a distributed model predictive control methodology to manage energy resources for a set of consumer subsystems. The objective of the controller is to optimally distribute the allowable energy to the subsystems. The proposed methodology yields a distributed solution that converges to the optimum that would be obtained by a centralized controller. This optimal performance is achieved by expressing the problem in terms of slack variables and the global coupling constraint as a set of local subsystem constraints, thereby favoring the application of distributed model predictive control. Hardware-in-the-loop experiments with an air-conditioning thermal solar plant are performed to show the good performance of the proposed distributed controller.This section presents the MPC formulation for resource-constrained systems, followed by a compact formulation and its reformulation in terms of slack variables that will be handy to develop a DMPC solution. Model predictive control formulationMPC encompasses the large class of control algorithms that make explicit use of a process model to obtain the control signal by minimizing an objective function over a given horizon [1]. Moreover, MPC strategies use a receding strategy whereby the horizon is displaced toward the future at each instant, but only the first control signal of the sequence calculated at each step is applied to the plant.

Relaxed hybrid consensus ADMM for distributed convex optimisation with coupling constraints

“…However, these distributed strategies are not suitable when the coupling arises from the constraints. To the best of our knowledge, DMPC algorithms for the distributed problems with global inequality constraints are somewhat limited [41, 42]. Therefore, it seems to be crucial to develop efficient and reliable fully decentralised algorithms to deal with such a complex problem.…”

Decompositions for MPC of Linear Dynamic Systems with Activation Constraints

“…Several systems found in the industry and society emerge from the interconnection of dynamic subsystems that share limited resources [1,2]. Representative systems include stations for recharging electric vehicles, energy building management, and cooling fluid distribution in buildings, among others.…”

A Computational Analysis of a Bilevel Decomposition for MPC of Resource Constrained Dynamic Systems

“…Several systems found in industry and society emerge from the interconnection of dynamic subsystems that share limited resources (Scherer et al, 2013(Scherer et al, , 2015. Representative systems include the charging of batteries of electric vehicles at stations, the energy management of buildings, and the distribution of cooling fluid in buildings, among others.…”