Distributed Model Predictive Control of linear discrete-time systems with local and global constraints

Wang, Zheming; Ong, Chong Jin

doi:10.1016/j.automatica.2017.03.027

Cited by 68 publications

(55 citation statements)

References 38 publications

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“…The technique proposed in this document aims to control a set of linear time-invariant systems that are coupled by an input constraint (which is related to an energy limitation). This problem has been tackled before using other distributed methods, e.g., Lagrangian relaxation of coupling constraints [20], alternating direction method of multipliers (ADMM) [21], local planning optimization and constraint tightening [22], and ADMM with early stopping criterion based on finite time consensus [23]. However, reported methods require either a centralized coordinator [21] or high communications load.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary‐games approach for distributed predictive control involving resource allocation

Barreiro-Gómez

Obando²,

Ocampo‐Martínez

et al. 2019

IET Control Theory & Applications

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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.

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Section: Introductionmentioning

confidence: 99%

Evolutionary‐games approach for distributed predictive control involving resource allocation

Barreiro-Gómez

Obando²,

Ocampo‐Martínez

et al. 2019

IET Control Theory & Applications

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“…MPC-Algorithmen basierend auf ADMM finden sich mittlerweile in zahlreichen Arbeiten [37,12,28,6,1,8,25].…”

Section: Literatur Erweiterungen and Anwendungsbeispieleunclassified

Verteilte Optimierung: Anwendungen in der Modellprädiktiven Regelung

Braun

Grüne

2018

At - Automatisierungstechnik

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Zusammenfassung Verteilte Optimierungsverfahren wie die duale Dekomposition oder die Alternating Direction Method of Multipliers (ADMM) erleben in den letzten Jahren ein erneutes steigendes Interesse in den unterschiedlichsten Anwendungen. Die zunehmende Vernetzung von Servern oder Mikrocontrollern weltweit sowie die Größe von heutigen Datensätzen liefern dabei die Grundlage für die Nachfrage nach iterativen, parallelisierbaren Optimierungsverfahren. In dieser Arbeit stellen wir verteilte Optimierungsalgorithmen und ihre Anwendungen bei der Berechnung von Zustandsrückführungen mithilfe der Modellprädiktiven Regelung vor. Wir konzentrieren uns auf die Systemdynamik sowie die Vernetzung der Systeme bei der Anwendbarkeit der Algorithmen. Darüber hinaus untersuchen wir die Algorithmen auf ihre Kommunikationsstruktur, den Austausch sensibler Daten, die Skalierbarkeit und die Flexibilität.

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“…Distributed model predictive control (DMPC), which controls each subsystem by an individual local model predictive control (MPC), has been one of the most important distributed control and optimization methods . It is because that DMPC not only inherits MPC's abilities to obtain good optimization performance, explicitly accommodating constraints, but also has the advantages of a distributed framework mentioned above . Thus, this paper will conduct a study based on DMPC.…”

Section: Introductionmentioning

confidence: 99%

Enhanced information reconfiguration for distributed model predictive control for cyber‐physical networked systems

Wei

Zheng

2019

Intl J Robust & Nonlinear

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Summary This paper considers a class of cyber‐physical networked systems, which are composed of many interacted subsystems, and are controlled in a distributed framework. The operating point of each subsystem changes with the varying of working conditions or productions, which may cause the change of the interactions among subsystems correspondingly. How to adapt to this change with good closed‐loop optimization performance and appropriate information connections is a problem. To solve this problem, the impaction of a subsystem's control action on the performance of related closed‐loop subsystems is first deduced for measuring the coupling among subsystems. Then, a distributed model predictive control (MPC) for tracking, whose subsystems online reconfigure their information structures, is proposed based on this impaction index. When the operating points changed, each local MPC calculates the impaction indices related to its structural downstream subsystems. If and only if the impaction index exceeds a defined bound, its behavior is considered by its downstream subsystem's MPC. The aim is to improve the optimization performance of entire closed‐loop systems and avoid the unnecessary information connections among local MPCs. Besides, contraction constraints are designed to guarantee that the overall system converges to the set points. The stability analysis is also provided. Simulation results show that the proposed impaction index is reasonable along with the efficiency of the proposed distributed MPC.

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Distributed Model Predictive Control of linear discrete-time systems with local and global constraints

Cited by 68 publications

References 38 publications

Evolutionary‐games approach for distributed predictive control involving resource allocation

Evolutionary‐games approach for distributed predictive control involving resource allocation

Verteilte Optimierung: Anwendungen in der Modellprädiktiven Regelung

Enhanced information reconfiguration for distributed model predictive control for cyber‐physical networked systems

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