TR2018-013

Leader-to-formation stability of multi-agent systems: An adaptive optimal control approach


    •  Gao, W., Jiang, Z.-P., Lewis, F., Wang, Y., "Leader-to-formation stability of multi-agent systems: An adaptive optimal control approach", IEEE Transactions on Automatic Control, DOI: 10.1109/TAC.2018.2799526, January 2018.
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      • @article{Gao2018jan,
      • author = {Gao, Weinan and Jiang, Zhong-Ping and Lewis, Frank and Wang, Yebin},
      • title = {Leader-to-formation stability of multi-agent systems: An adaptive optimal control approach},
      • journal = {IEEE Transactions on Automatic Control},
      • year = 2018,
      • month = jan,
      • doi = {10.1109/TAC.2018.2799526},
      • url = {https://www.merl.com/publications/TR2018-013}
      • }
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    Control


This note proposes a novel data-driven solution to the cooperative adaptive optimal control problem of leaderfollower multi-agent systems under switching network topology. The dynamics of all the followers are unknown, and the leader is modeled by a perturbed exosystem. Through the combination of adaptive dynamic programming and internal model principle, an approximate optimal controller is iteratively learned online using real-time input-state data. Rigorous stability analysis shows that the system in closed-loop with the developed control policy is leader-to-formation stable, with guaranteed robustness to unmeasurable leader disturbance. Numerical results illustrate the effectiveness of the proposed data-driven algorithm.