TR2017-091
Distributed Extremum Seeking in Multi-Agent Systems with Arbitrary Switching Graphs
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- "Distributed Extremum Seeking in Multi-Agent Systems with Arbitrary Switching Graphs", World Congress of the International Federation of Automatic Control (IFAC), DOI: 10.1016/j.ifacol.2017.08.240, July 2017, vol. 50, pp. 735-740.BibTeX TR2017-091 PDF
- @inproceedings{Poveda2017jul,
- author = {Poveda, Jorge and Benosman, Mouhacine and Teel, Andy},
- title = {Distributed Extremum Seeking in Multi-Agent Systems with Arbitrary Switching Graphs},
- booktitle = {World Congress of the International Federation of Automatic Control (IFAC)},
- year = 2017,
- volume = 50,
- number = 1,
- pages = {735--740},
- month = jul,
- publisher = {Elsevier},
- doi = {10.1016/j.ifacol.2017.08.240},
- url = {https://www.merl.com/publications/TR2017-091}
- }
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- "Distributed Extremum Seeking in Multi-Agent Systems with Arbitrary Switching Graphs", World Congress of the International Federation of Automatic Control (IFAC), DOI: 10.1016/j.ifacol.2017.08.240, July 2017, vol. 50, pp. 735-740.
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Abstract:
This paper studies the problem of averaging-based extremum seeking in dynamical multi-agent systems with time-varying communication graphs. We consider a distributed consensus-optimization problem where the plants and the controllers of the agents share information via time-varying graphs, and where the cost function to be minimized corresponds to the summation of the individual response maps generated by the agents. Although the problem of averaging-based extremum seeking control in multi-agent dynamical systems with timeinvariant graphs has been extensively studied, the case where the graph is time-varying remains unexplored. In this paper we address this problem by making use of recent results for generalized set-valued hybrid extremum seeking controllers, and the framework of switched differential inclusions and common Lyapunov functions. For the particular consensus-optimization problem considered in this paper, a semi-global practical stability result is established. A numerical example in the context of dynamic electricity markets illustrates the results.