Adaptive Consensus with Exponential Decay
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908398992228352 |
|---|---|
| author | Choi, Woocheol Jang, Piljae |
| author_facet | Choi, Woocheol Jang, Piljae |
| contents | This paper addresses the adaptive consensus problem in uncertain multi-agent systems, particularly under challenges posed by quantized communication. We consider agents with general linear dynamics subject to nonlinear uncertainties and propose an adaptive consensus control framework that integrates concurrent learning. Unlike traditional methods relying solely on instantaneous data, concurrent learning leverages stored historical data to enhance parameter estimation without requiring persistent excitation. We establish that the proposed controller ensures exponential convergence of both consensus and parameter estimation. Furthermore, we extend the analysis to scenarios where inter-agent communication is quantized using a uniform quantizer. We prove that the system still achieves consensus up to an error proportional to the quantization level, with exponential convergence rate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07203 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Consensus with Exponential Decay Choi, Woocheol Jang, Piljae Optimization and Control This paper addresses the adaptive consensus problem in uncertain multi-agent systems, particularly under challenges posed by quantized communication. We consider agents with general linear dynamics subject to nonlinear uncertainties and propose an adaptive consensus control framework that integrates concurrent learning. Unlike traditional methods relying solely on instantaneous data, concurrent learning leverages stored historical data to enhance parameter estimation without requiring persistent excitation. We establish that the proposed controller ensures exponential convergence of both consensus and parameter estimation. Furthermore, we extend the analysis to scenarios where inter-agent communication is quantized using a uniform quantizer. We prove that the system still achieves consensus up to an error proportional to the quantization level, with exponential convergence rate. |
| title | Adaptive Consensus with Exponential Decay |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2506.07203 |