Self-Triggered Predefined-Time Cooperative Control Against DoS Attacks for Multiagent Systems With Uncertain Powers
This article studies the predefined-time cooperative control based on self-triggered mechanism for the multiagent system with uncertain powers and denial-of-service (DoS) attacks. Unlike the majority of existing results with input powers of one, the system's powers considered in this study are uncertain odd numbers greater than one. A distributed observer in the first-order filter form is designed to estimate unmeasurable states under DoS attacks. Subsequently, by constructing a transform function, we establish a new distributed predefined time control law based on the neural network approximation approach. Different from the working theory of the event-triggered mechanism, the triggering moment of self-triggered mechanism introduced in this article is determined by the previous moment's state, which further decreases the resource wastage. Compared with finite-time and fixed-time control, the distributed predefined-time control protocol developed in this article can guarantee that system's synchronization errors are steered to a preset range within a predefined time, and the predefined time and range can be set freely by the user. Finally, the theoretical design is verified by some simulation results.
Authors
- Qikun Shen (ORCID: https://orcid.org/0000-0002-1749-2641)
- Shuhui Bi (ORCID: https://orcid.org/0000-0002-2832-4985)
- Yadong Yang (ORCID: https://orcid.org/0009-0004-4810-0606)
- Rui Dai (ORCID: https://orcid.org/0000-0001-5477-8078)
Institutions
- Fuyang Normal University (CN)
- Aeronautical Development Establishment (IN)
- Chinese Academy of Civil Aviation Science and Technology (CN)
- Chinese Aeronautical Establishment (CN)
- Yangzhou University (CN)
Publication Details
- Journal
- IEEE Transactions on Cybernetics
- Published
- 2025-06-24
- DOI
- https://doi.org/10.1109/tcyb.2025.3574331
- Citations
- 4
- Primary Topic
- Fault Detection and Control Systems
- Type
- article
- Field-Weighted Citation Impact
- 4.86
Funders
- National Natural Science Foundation of China