TR2017-064

Cooperative optimal output regulation of multi-agent systems using adaptive dynamic programming

    •  Gao, W., Jiang, Z.-P., Lewis, F., Wang, Y., "Cooperative optimal output regulation of multi-agent systems using adaptive dynamic programming", American Control Conference (ACC), DOI: 10.23919/​ACC.2017.7963356, May 2017.
      BibTeX TR2017-064 PDF
      • @inproceedings{Gao2017may,
      • author = {Gao, Weinan and Jiang, Zhong-Ping and Lewis, Frank and Wang, Yebin},
      • title = {Cooperative optimal output regulation of multi-agent systems using adaptive dynamic programming},
      • booktitle = {American Control Conference (ACC)},
      • year = 2017,
      • month = may,
      • doi = {10.23919/ACC.2017.7963356},
      • url = {https://www.merl.com/publications/TR2017-064}
      • }
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  • Research Area:

    Control

Abstract:

This paper proposes a novel solution to the adaptive optimal output regulation problem of continuoustime linear multi-agent systems. A key strategy is to resort to reinforcement learning and approximate/adaptive dynamic programming. A data-driven, non-model-based algorithm is given to design a distributed adaptive suboptimal output regulator in the presence of unknown system dynamics. The effectiveness of the proposed computational control algorithm is demonstrated via cooperative adaptive cruise control of connected and autonomous vehicles.