NDLI: Robust adaptive control of coupled stochastic multi-agent systems with unmodeled dynamics

Content Provider	IEEE Xplore Digital Library
Author	Qiang Zhang Ji-Feng Zhang
Copyright Year	2010
Description	Author affiliation: Key Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China (Qiang Zhang; Ji-Feng Zhang)
Abstract	Distributed robust adaptive control is investigated for a class of stochastic multi-agent systems with unknown time-varying structure parameters, unmodeled dynamics, external disturbances, and interactions among agents. The control goal is to make the states of all the agents converge to a desired function of the population state average (PSA). Due to the fact that only local information is available for each agent, the control is distributed. The key techniques adopted here to cope with the uncertainties are normalized projected least mean square algorithm, Nash certainty equivalence principle and certainty equivalence principle, which are used for estimating the unknown time-varying parameters and unknown PSA term, and designing adaptive control, respectively. Under some mild conditions, the stability of the closed-loop system, consistence of the PSA estimate, and robust Nash equilibrium property of the control laws are obtained. A numerical example is given to illustrate the results.
Starting Page	586
Ending Page	591
File Size	648931
Page Count	6
File Format	PDF
ISBN	9781424467129
e-ISBN	9781424467129
DOI	10.1109/WCICA.2010.5553940
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Adaptive control Nash equilibrium Performance analysis Multiagent systems Adaptation model Control systems Multi-agent system Stochastic system Robust adaptive control Distributed control
Content Type	Text
Resource Type	Article

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