Abstract
This paper first proposes a distributed continuous-time Newton-Raphson algorithm for heterogeneous linear multi-agent systems over unbalanced digraphs. Then this approach extends to cases where the local cost functions and Hessian matrices are unknown. While local exponential stability of the inverse Hessian matrix estimator has been established for single-agent systems, this paper proves local exponential stability in multi-agent systems, ensuring the stability of the proposed distributed Newton-Raphson extremum seeking algorithm. A numerical example demonstrates the effectiveness of the proposed algorithms. © The Editorial Office of JSSC & Springer-Verlag GmbH Germany 2025.
| Original language | English |
|---|---|
| Pages (from-to) | 902-918 |
| Journal | Journal of Systems Science and Complexity |
| Volume | 38 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - Apr 2025 |
Funding
This research was supported in part by the National Natural Science Foundation of China (NSFC) under Grant No. 62373314; in part by the Research Grants Council of the Hong Kong Special Administrative Region of China under Project CityU/11207323; and in part by the NSFC-Excellent Young Scientists Fund (Hong Kong and Macao) under Grant No. 62222318.
Research Keywords
- Distributed optimization
- extremum seeking
- multi-agent systems
- Newton-Raphson method
- unbalanced digraphs
RGC Funding Information
- RGC-funded
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GRF: Distributed Optimal Self-Deployment Control of Multiple Dynamic Systems subject to Various Uncertainties and Communication Constraints
LIU, L. (Principal Investigator / Project Coordinator)
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