Decentralized Robust Adaptive Control for the Multiagent System Consensus Problem Using Neural Networks

A robust adaptive control approach is proposed to solve the consensus problem of multiagent systems. Compared with the previous work, the agent's dynamics includes the uncertainties and external disturbances, which is more practical in real-world applications. Due to the approximation capabilit...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2009-06, Vol.39 (3), p.636-647
Main Authors: Hou, Zeng-Guang, Cheng, Long, Tan, Min
Format: Article
Language:English
Subjects:
Adaptive
Adaptive control
Approximation
Communication switching
Computer simulation
consensus
Cybernetics
Distributed control
Disturbances
Dynamical systems
Dynamics
Mathematical analysis
multiagent system
Multiagent systems
Network topology
Neural networks
robust
Robust control
Robustness
Uncertainty
Vehicle dynamics
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Summary:A robust adaptive control approach is proposed to solve the consensus problem of multiagent systems. Compared with the previous work, the agent's dynamics includes the uncertainties and external disturbances, which is more practical in real-world applications. Due to the approximation capability of neural networks, the uncertain dynamics is compensated by the adaptive neural network scheme. The effects of the approximation error and external disturbances are counteracted by employing the robustness signal. The proposed algorithm is decentralized because the controller for each agent only utilizes the information of its neighbor agents. By the theoretical analysis, it is proved that the consensus error can be reduced as small as desired. The proposed method is then extended to two cases: agents form a prescribed formation, and agents have the higher order dynamics. Finally, simulation examples are given to demonstrate the satisfactory performance of the proposed method.
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/TSMCB.2008.2007810