Neural-network-based adaptive leader-following control for multiagent systems with uncertainties

Authors:
Long Cheng;Zeng-Guang Hou;Min Tan;Yingzi Lin;Wenjun Zhang
Affiliations:
Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Department of Mechanical and Industrial Engineering, College of Engineering, Northeastern University, Boston, MA;Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
Venue:
IEEE Transactions on Neural Networks
Year:
2010

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Abstract

A neural-network-based adaptive approach is proposed for the leader-following control of multiagent systems. The neural network is used to approximate the agent's uncertain dynamics, and the approximation error and external disturbances are counteracted by employing the robust signal. When there is no control input constraint, it can be proved that all the following agents can track the leader's time-varying state with the tracking error as small as desired. Compared with the related work in the literature, the uncertainty in the agent's dynamics is taken into account; the leader's state could be time-varying; and the proposed algorithm for each following agent is only dependent on the information of its neighbor agents. Finally, the satisfactory performance of the proposed method is illustrated by simulation examples.