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Output‐feedback adaptive consensus tracking control for a class of high‐order nonlinear multi‐agent systems
Author(s) -
Wang Chenliang,
Wen Changyun,
Wang Wei,
Hu Qinglei
Publication year - 2017
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.3837
Subject(s) - control theory (sociology) , nonlinear system , bounded function , tracking error , computer science , quantization (signal processing) , multi agent system , residual , class (philosophy) , tracking (education) , set (abstract data type) , mathematics , control (management) , artificial intelligence , algorithm , psychology , mathematical analysis , pedagogy , physics , quantum mechanics , programming language
Summary In this paper, an output‐feedback adaptive consensus tracking control scheme is proposed for a class of high‐order nonlinear multi‐agent systems. The agents are allowed to have unknown parameters, unknown nonlinearities, and input quantization simultaneously. The desired trajectory to be tracked is available for only a subset of agents, and only the relative outputs and the quantized inputs need to be measured or transmitted as signal exchange among neighbors regardless of the system order. By introducing a kind of high‐gain K‐filters and a smooth function, the effect among agents caused by the unknown nonlinearities is successfully counteracted, and all closed‐loop signals are proved to be globally uniformly bounded. Moreover, it is shown that the tracking errors converge to a residual set that can be made arbitrarily small. Simulation results on robot manipulators are presented to illustrate the effectiveness of the proposed scheme. Copyright © 2017 John Wiley & Sons, Ltd.