A Distributed Consensus Scenario Approach to Optimization and Control With Uncertainties
This paper addresses the problem of distributed optimization with uncertainties for multiagent systems. We propose a new distributed consensus scenario approach, handling uncertainties through a scenario program method. An approximate solution is developed, followed by a distributed estimation algor...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10824804/ |
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| author | Jing Wang Steven Drager |
| author_facet | Jing Wang Steven Drager |
| author_sort | Jing Wang |
| collection | DOAJ |
| description | This paper addresses the problem of distributed optimization with uncertainties for multiagent systems. We propose a new distributed consensus scenario approach, handling uncertainties through a scenario program method. An approximate solution is developed, followed by a distributed estimation algorithm to manage large data volumes without a centralized approach. Our design inherently offers robustness against adversarial attacks and preserves data privacy. We provide rigorous convergence analysis and demonstrate the effectiveness of our approach through application examples in regression and robust internal model control. This work presents a significant step towards holistic solutions for optimization in safety-critical multiagent systems. |
| format | Article |
| id | doaj-art-589af34486ab44de86613e6c3ac208d4 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-589af34486ab44de86613e6c3ac208d42025-01-10T00:01:28ZengIEEEIEEE Access2169-35362025-01-01135308532010.1109/ACCESS.2025.352596310824804A Distributed Consensus Scenario Approach to Optimization and Control With UncertaintiesJing Wang0https://orcid.org/0000-0002-0114-1513Steven Drager1Department of Electrical Engineering, Illinois State University, Normal, IL, USAAir Force Research Laboratory, Rome, NY, USAThis paper addresses the problem of distributed optimization with uncertainties for multiagent systems. We propose a new distributed consensus scenario approach, handling uncertainties through a scenario program method. An approximate solution is developed, followed by a distributed estimation algorithm to manage large data volumes without a centralized approach. Our design inherently offers robustness against adversarial attacks and preserves data privacy. We provide rigorous convergence analysis and demonstrate the effectiveness of our approach through application examples in regression and robust internal model control. This work presents a significant step towards holistic solutions for optimization in safety-critical multiagent systems.https://ieeexplore.ieee.org/document/10824804/Optimization with uncertaintiesdistributed algorithmsscenario approachconsensus algorithmmultiagentdistributed learning |
| spellingShingle | Jing Wang Steven Drager A Distributed Consensus Scenario Approach to Optimization and Control With Uncertainties IEEE Access Optimization with uncertainties distributed algorithms scenario approach consensus algorithm multiagent distributed learning |
| title | A Distributed Consensus Scenario Approach to Optimization and Control With Uncertainties |
| title_full | A Distributed Consensus Scenario Approach to Optimization and Control With Uncertainties |
| title_fullStr | A Distributed Consensus Scenario Approach to Optimization and Control With Uncertainties |
| title_full_unstemmed | A Distributed Consensus Scenario Approach to Optimization and Control With Uncertainties |
| title_short | A Distributed Consensus Scenario Approach to Optimization and Control With Uncertainties |
| title_sort | distributed consensus scenario approach to optimization and control with uncertainties |
| topic | Optimization with uncertainties distributed algorithms scenario approach consensus algorithm multiagent distributed learning |
| url | https://ieeexplore.ieee.org/document/10824804/ |
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