Details
Original language | English |
---|---|
Pages (from-to) | 368-379 |
Number of pages | 12 |
Journal | Automatica |
Volume | 96 |
Early online date | 1 Aug 2018 |
Publication status | Published - 1 Oct 2018 |
Externally published | Yes |
Abstract
In this paper, we consider the problem of coordinating self-interested interacting dynamical systems by means of a distributed economic MPC framework. The self-interest of the systems is reflected by an individual local objective function each agent is trying to minimize, while cooperation is required with respect to coupling constraints and an asymptotic cooperative goal, which is represented by a particular steady state of the overall system. Our basic premise is that this steady state, which fulfills the cooperative goal, is not known a priori but has to be negotiated online, while already taking control actions. For the purpose of determining this steady state in a distributed way, arbitrary distributed computation algorithms can be incorporated into the proposed framework. We show that satisfaction of coupling constraints and convergence to the desired overall steady state can be established. Examples for an asymptotic cooperative goal include synchronization under conflicting objectives or sensor coverage, which are both studied in the work at hand and are also illustrated by numerical simulations.
Keywords
- Collaborative systems, Distributed model predictive control, Economic model predictive control
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Control and Systems Engineering
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In: Automatica, Vol. 96, 01.10.2018, p. 368-379.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A distributed economic MPC framework for cooperative control under conflicting objectives
AU - Köhler, Philipp N.
AU - Müller, Matthias A.
AU - Allgöwer, Frank
N1 - Funding information: The authors thank the German Research Foundation (DFG) for support of this work within grant AL 316/11-1 and within the Cluster of Excellence in Simulation Technology (EXC 310/2) at the University of Stuttgart. The material in this paper was partially presented at the 19th IFAC World Congress, August 24–29, 2014, Cape Town, South Africa and the 2016 American Control Conference, July 6–8, 2016, Boston, MA, USA. This paper was recommended for publication in revised form by Associate Editor Giancarlo Ferrari-Trecate under the direction of Editor Ian R. Petersen.
PY - 2018/10/1
Y1 - 2018/10/1
N2 - In this paper, we consider the problem of coordinating self-interested interacting dynamical systems by means of a distributed economic MPC framework. The self-interest of the systems is reflected by an individual local objective function each agent is trying to minimize, while cooperation is required with respect to coupling constraints and an asymptotic cooperative goal, which is represented by a particular steady state of the overall system. Our basic premise is that this steady state, which fulfills the cooperative goal, is not known a priori but has to be negotiated online, while already taking control actions. For the purpose of determining this steady state in a distributed way, arbitrary distributed computation algorithms can be incorporated into the proposed framework. We show that satisfaction of coupling constraints and convergence to the desired overall steady state can be established. Examples for an asymptotic cooperative goal include synchronization under conflicting objectives or sensor coverage, which are both studied in the work at hand and are also illustrated by numerical simulations.
AB - In this paper, we consider the problem of coordinating self-interested interacting dynamical systems by means of a distributed economic MPC framework. The self-interest of the systems is reflected by an individual local objective function each agent is trying to minimize, while cooperation is required with respect to coupling constraints and an asymptotic cooperative goal, which is represented by a particular steady state of the overall system. Our basic premise is that this steady state, which fulfills the cooperative goal, is not known a priori but has to be negotiated online, while already taking control actions. For the purpose of determining this steady state in a distributed way, arbitrary distributed computation algorithms can be incorporated into the proposed framework. We show that satisfaction of coupling constraints and convergence to the desired overall steady state can be established. Examples for an asymptotic cooperative goal include synchronization under conflicting objectives or sensor coverage, which are both studied in the work at hand and are also illustrated by numerical simulations.
KW - Collaborative systems
KW - Distributed model predictive control
KW - Economic model predictive control
UR - http://www.scopus.com/inward/record.url?scp=85050793166&partnerID=8YFLogxK
U2 - 10.1016/j.automatica.2018.07.001
DO - 10.1016/j.automatica.2018.07.001
M3 - Article
VL - 96
SP - 368
EP - 379
JO - Automatica
JF - Automatica
SN - 0005-1098
ER -