Distributed control of multi-agent systems over unknown communication networks using extremum seeking. (November 2017)

• Record Type: Journal Article
• Authors: Ebegbulem, Judith; Guay, Martin
• Is Part Of: Journal of process control. Volume 59(2017)
• Page Start: 37
• Page End: 48
• Publication Date: 2017-11
• Subjects: Distributed control -- Extremum seeking control -- Consensus estimation -- Unknown communication networks -- Real-time optimization -- Multi-agent systems; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2017.09.002
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: The manuscript proposes a novel distributed extremum seeking control for the solution of distributed optimization problems. The optimization is solved over an unknown communication network in which each agent of the network has access to a local cost measurement. The optimization problem achieves the consensus of all agents to the minimizer of the sum of the local costs. A detailed convergence analysis of the network optimization problem is provided. Abstract: In this paper, the solution of large-scale real-time optimization problems of multi-agent systems (MAS) is tackled in a distributed and a cooperative manner without the requirement of exact knowledge of network connectivity. Each agent in the communication network measures a local disagreement cost in addition to its local cost. The agents must work collaboratively to ensure that the system's unknown overall cost (i.e., the sum of the local cost of all the agents) is minimized. In order to minimize this cost, the local disagreement cost of all the agents must first be minimized. This minimization requires the solution of a consensus estimation problem and ensures that the agents reach agreement on their decision variables. To address this challenging problem, a distributed proportional-integral extremum seeking control technique is proposed, one that solves both problems simultaneously. Three simulation examples are included, they demonstrate the effectiveness and robustness of the proposed technique.