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Learning-Based distributed resilient Fault-Tolerant control method for heterogeneous MASs under unknown leader dynamic

Deng, C., Jin, X-Z, Che, W-W and Wang, H.ORCID: 0000-0003-2789-9530 (2021) Learning-Based distributed resilient Fault-Tolerant control method for heterogeneous MASs under unknown leader dynamic. IEEE Transactions on Neural Networks and Learning Systems . Early Access.

Link to Published Version: https://doi.org/10.1109/TNNLS.2021.3070869
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Abstract

In this article, we consider the distributed fault-tolerant resilient consensus problem for heterogeneous multiagent systems (MASs) under both physical failures and network denial-of-service (DoS) attacks. Different from the existing consensus results, the dynamic model of the leader is unknown for all followers in this article. To learn this unknown dynamic model under the influence of DoS attacks, a distributed resilient learning algorithm is proposed by using the idea of data-driven. Based on the learned dynamic model of the leader, a distributed resilient estimator is designed for each agent to estimate the states of the leader. Then, a new adaptive fault-tolerant resilient controller is designed to resist the effect of physical failures and network DoS attacks. Moreover, it is shown that the consensus can be achieved with the proposed learning-based fault-tolerant resilient control method. Finally, a simulation example is provided to show the effectiveness of the proposed method.

Item Type: Journal Article
Murdoch Affiliation(s): Engineering and Energy
Publisher: IEEE
Copyright: © 2021 IEEE
URI: http://researchrepository.murdoch.edu.au/id/eprint/60626
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