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 -