Robust Model Reference Adaptive Consensus with Neural Networks

Conference Paper (2022)
Author(s)

D. Yue (Southeast University, TU Delft - Team Bart De Schutter)

S Baldi (TU Delft - Team Bart De Schutter, Southeast University)

Jinde Cao (Southeast University)

Research Group
Team Bart De Schutter
DOI related publication
https://doi.org/10.1109/CCDC55256.2022.10033441
More Info
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Publication Year
2022
Language
English
Research Group
Team Bart De Schutter
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
2503-2508
ISBN (electronic)
978-1-6654-7896-0
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Abstract

This paper addresses distributed and robust leaderless consensus control for a class of uncertain multiagent systems with matched unknown nonlinearities and disturbances. The problem is challenging due to the lack of a leader (reference signal), the large uncertainties in agent dynamics, and the asymmetric communications among the agents. A novel neural network embedded model reference adaptive consensus (NN-MRACon) framework is proposed, which bridges NN and MRACon by means of nonsmooth control. Asymptotic consensus is proved based on robust analysis and input-to-state stability theory. Numerical examples on networks of second-order integrators and two-mass-spring systems are included to validate the effectiveness of NN-MRACon.

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