TR2019-148

CODES: Cooperative Data-Enabled Extremum Seeking for Multi-Agent Systems


    •  Poveda, J., Vamvoudakis, K., Benosman, M., "CODES: Cooperative Data-Enabled Extremum Seeking for Multi-Agent Systems", IEEE Conference on Decision and Control (CDC), December 2019.
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      • @inproceedings{Poveda2019dec,
      • author = {Poveda, Jorge and Vamvoudakis, Kyriakos and Benosman, Mouhacine},
      • title = {CODES: Cooperative Data-Enabled Extremum Seeking for Multi-Agent Systems},
      • booktitle = {IEEE Conference on Decision and Control (CDC)},
      • year = 2019,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2019-148}
      • }
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  • Research Area:

    Optimization


In this paper, we study the problem of modelfree cooperative real-time optimization in multi-agent network systems (MAS). Unlike existing adaptive extremum seeking approaches that presume the satisfaction of a persistence of excitation condition on the agents of the network, we propose a novel approach that leverages the presence of cooperation and information-rich data sets in the system. This approach is based on the idea that in MAS with sufficient communication and information resources, agents can efficiently learn a common cost function under mild individual excitation requirements by leveraging cooperation. Therefore, our main result can be seen as a spatiotemporal condition that guarantees model-free optimization in MAS with agents having homogeneous but unknown cost functions. To solve this model-free optimization problem, we characterize a class of robust dynamics that can be safely interconnected with the data-enabled learning mechanism in order to achieve a stable closed-loop system. A numerical result is presented to illustrate the approach.