Abstract
In this work, we present a new distributed adaptive iterative learning control (AILC) scheme for a class of high-order nonlinear multi-agent systems (MAS) under alignment condition with both parametric and nonparametric system uncertainties, where the actuators may be faulty and the control input gain functions are not fully known. Nonparametric uncertainties such as norm-bounded nonlinear uncertainties can be effectively handled. Backstepping design with the composite energy function (CEF) structure is used in the discussion. Through rigorous analysis, we show that under this new AILC scheme, uniform convergence of agents output tracking error over the iteration domain is guaranteed. In the end, an illustrative example is presented to demonstrate the efficacy of the proposed AILC scheme.
Original language | English |
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Title of host publication | 2016 American Control Conference, ACC 2016 |
Pages | 1253-1258 |
Number of pages | 6 |
ISBN (Electronic) | 9781467386821 |
DOIs | |
State | Published - Jul 28 2016 |
Event | 2016 American Control Conference, ACC 2016 - Boston, United States Duration: Jul 6 2016 → Jul 8 2016 |
Publication series
Name | Proceedings of the American Control Conference |
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Volume | 2016-July |
ISSN (Print) | 0743-1619 |
Conference
Conference | 2016 American Control Conference, ACC 2016 |
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Country/Territory | United States |
City | Boston |
Period | 7/6/16 → 7/8/16 |
Bibliographical note
Publisher Copyright:© 2016 American Automatic Control Council (AACC).
ASJC Scopus subject areas
- Electrical and Electronic Engineering