Prescribed-time Fault-tolerant Consensus for Uncertain Nonlinear Multi-agent Systems
Abstract
Achieving consensus within a user-defined time frame for uncertain nonlinear systems is both crucial and challenging. To tackle this issue, we propose an adaptive consensus protocol that utilizes a radial basis function neural network to handle unknown nonlinearities and actuator faults. Unlike traditional finite-time or fixed-time consensus methods, our approach employs continuous, time-varying feedback to guarantee convergence within the desired time. The proposed strategy ensures that all closed-loop signals of the system remain bounded, achieving consensus within the prescribed time. The effectiveness of the proposed control strategy is demonstrated through a simulation example of phase synchronization in a power system.
Recommended Citation
V. K. Singh and J. Sarangapani, "Prescribed-time Fault-tolerant Consensus for Uncertain Nonlinear Multi-agent Systems," IFAC Journal of Systems and Control, vol. 32, article no. 100313, Elsevier, Jun 2025.
The definitive version is available at https://doi.org/10.1016/j.ifacsc.2025.100313
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Keywords and Phrases
Adaptive control; Distributed power grid; Multi-agent system; Phase synchronization; Prescribed time; RBF neural network
International Standard Serial Number (ISSN)
2468-6018
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Elsevier, All rights reserved.
Publication Date
01 Jun 2025
Comments
Office of Naval Research, Grant N00014-24-1-2338