Journal article
Learning-Based distributed resilient Fault-Tolerant control method for heterogeneous MASs under unknown leader dynamic
IEEE Transactions on Neural Networks and Learning Systems, Vol.33(10), pp.5504-5513
2022
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.
Details
- Title
- Learning-Based distributed resilient Fault-Tolerant control method for heterogeneous MASs under unknown leader dynamic
- Authors/Creators
- C. Deng (Author/Creator) - Qilu University of TechnologyX-Z Jin (Author/Creator) - Shandong Academy of SciencesW-W Che (Author/Creator) - Qingdao UniversityH. Wang (Author/Creator) - Murdoch University
- Publication Details
- IEEE Transactions on Neural Networks and Learning Systems, Vol.33(10), pp.5504-5513
- Publisher
- IEEE
- Identifiers
- 991005542822307891
- Copyright
- © 2021 IEEE
- Murdoch Affiliation
- School of Engineering and Energy
- Language
- English
- Resource Type
- Journal article
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites
Metrics
28 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.29 Automation & Control Systems
- 4.29.435 Multi Agent Systems
- Web Of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Hardware & Architecture
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic
- ESI research areas
- Computer Science