TY - JOUR
T1 - Leader-to-Formation Stability of Multiagent Systems
T2 - An Adaptive Optimal Control Approach
AU - Gao, Weinan
AU - Jiang, Zhong Ping
AU - Lewis, Frank L.
AU - Wang, Yebin
N1 - Funding Information:
Manuscript received September 3, 2017; revised November 10, 2017; accepted January 15, 2018. Date of publication January 30, 2018; date of current version September 25, 2018. This work was supported in part by the U.S. National Science Foundation under Grant ECCS-1501044 and in part by Mitsubishi Electric Research Laboratories. The work of F. L. Lewis was supported in part by China NSFC under Grant #61633007 and in part by ONR under Grant N00014-17-1-2239. Recommended by Associate Editor Z. Chen. (Corresponding author: Weinan Gao.) W. Gao is with the Department of Electrical and Computer Engineering, Allen E. Paulson College of Engineering and Computing, Georgia Southern University, Statesboro, GA 30460 USA (e-mail: wgao@ georgiasouthern.edu).
Publisher Copyright:
© 1963-2012 IEEE.
PY - 2018/10
Y1 - 2018/10
N2 - This note proposes a novel data-driven solution to the cooperative adaptive optimal control problem of leader-follower multiagent 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.
AB - This note proposes a novel data-driven solution to the cooperative adaptive optimal control problem of leader-follower multiagent 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.
KW - Adaptive dynamic programming (ADP)
KW - leader-to-formation stability (LFS)
KW - optimal tracking control
KW - switching network topology
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U2 - 10.1109/TAC.2018.2799526
DO - 10.1109/TAC.2018.2799526
M3 - Article
AN - SCOPUS:85041411999
VL - 63
SP - 3581
EP - 3587
JO - IRE Transactions on Automatic Control
JF - IRE Transactions on Automatic Control
SN - 0018-9286
IS - 10
M1 - 8272371
ER -