Distributed Optimization for Fractional-Order Multi-Agent Systems Based on Adaptive Backstepping Dynamic Surface Control Technology
In this article, the distributed optimization problem is studied for a class of fractional-order nonlinear uncertain multi-agent systems (MASs) with unmeasured states. Each agent is represented through a system with unknown nonlinearities, unmeasurable states and a local objective function described...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Fractal and Fractional |
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| Online Access: | https://www.mdpi.com/2504-3110/6/11/642 |
| _version_ | 1797468244396736512 |
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| author | Xiaole Yang Weiming Zhao Jiaxin Yuan Tao Chen Chen Zhang Liangquan Wang |
| author_facet | Xiaole Yang Weiming Zhao Jiaxin Yuan Tao Chen Chen Zhang Liangquan Wang |
| author_sort | Xiaole Yang |
| collection | DOAJ |
| description | In this article, the distributed optimization problem is studied for a class of fractional-order nonlinear uncertain multi-agent systems (MASs) with unmeasured states. Each agent is represented through a system with unknown nonlinearities, unmeasurable states and a local objective function described by a quadratic polynomial function. A penalty function is constructed by a sum of local objective functions and integrating consensus conditions of the MASs. Radial basis function Neural-networks (RBFNNs) and Neural networks (NN) state observer are applied to approximate the unknown nonlinear dynamics and estimate unmeasured states, respectively. By combining the NN state observer and the penalty function, and the stability theory of the Lyapunov function, the distributed observer-based adaptive optimized backstepping dynamic surface control protocol is proposed to ensure the outputs of all agents asymptotically reach consensus to the optimal solution of the global objective function. Simulations demonstrate the effectiveness of the proposed control scheme. |
| first_indexed | 2024-03-09T19:03:41Z |
| format | Article |
| id | doaj.art-937deed1546b437bb2ed3597273f532c |
| institution | Directory Open Access Journal |
| issn | 2504-3110 |
| language | English |
| last_indexed | 2024-03-09T19:03:41Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj.art-937deed1546b437bb2ed3597273f532c2023-11-24T04:45:18ZengMDPI AGFractal and Fractional2504-31102022-11-0161164210.3390/fractalfract6110642Distributed Optimization for Fractional-Order Multi-Agent Systems Based on Adaptive Backstepping Dynamic Surface Control TechnologyXiaole Yang0Weiming Zhao1Jiaxin Yuan2Tao Chen3Chen Zhang4Liangquan Wang5School of Air Transportation, Shanghai University of Engineering Science, Shanghai 201620, ChinaLow Speed Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang 621000, ChinaSchool of Air Transportation, Shanghai University of Engineering Science, Shanghai 201620, ChinaCollege of Engineering, China Agricultural University, Beijing 169334, ChinaSchool of Aeronautics and Astronautics, Shanghai Jiao Tong University, Room 2410, Dongchuan Road No. 800, Shanghai 200240, ChinaLow Speed Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang 621000, ChinaIn this article, the distributed optimization problem is studied for a class of fractional-order nonlinear uncertain multi-agent systems (MASs) with unmeasured states. Each agent is represented through a system with unknown nonlinearities, unmeasurable states and a local objective function described by a quadratic polynomial function. A penalty function is constructed by a sum of local objective functions and integrating consensus conditions of the MASs. Radial basis function Neural-networks (RBFNNs) and Neural networks (NN) state observer are applied to approximate the unknown nonlinear dynamics and estimate unmeasured states, respectively. By combining the NN state observer and the penalty function, and the stability theory of the Lyapunov function, the distributed observer-based adaptive optimized backstepping dynamic surface control protocol is proposed to ensure the outputs of all agents asymptotically reach consensus to the optimal solution of the global objective function. Simulations demonstrate the effectiveness of the proposed control scheme.https://www.mdpi.com/2504-3110/6/11/642fractional order multiagent systems (FOMASs)distributed optimizationdynamic surface control (DSC)neural networksobserver |
| spellingShingle | Xiaole Yang Weiming Zhao Jiaxin Yuan Tao Chen Chen Zhang Liangquan Wang Distributed Optimization for Fractional-Order Multi-Agent Systems Based on Adaptive Backstepping Dynamic Surface Control Technology Fractal and Fractional fractional order multiagent systems (FOMASs) distributed optimization dynamic surface control (DSC) neural networks observer |
| title | Distributed Optimization for Fractional-Order Multi-Agent Systems Based on Adaptive Backstepping Dynamic Surface Control Technology |
| title_full | Distributed Optimization for Fractional-Order Multi-Agent Systems Based on Adaptive Backstepping Dynamic Surface Control Technology |
| title_fullStr | Distributed Optimization for Fractional-Order Multi-Agent Systems Based on Adaptive Backstepping Dynamic Surface Control Technology |
| title_full_unstemmed | Distributed Optimization for Fractional-Order Multi-Agent Systems Based on Adaptive Backstepping Dynamic Surface Control Technology |
| title_short | Distributed Optimization for Fractional-Order Multi-Agent Systems Based on Adaptive Backstepping Dynamic Surface Control Technology |
| title_sort | distributed optimization for fractional order multi agent systems based on adaptive backstepping dynamic surface control technology |
| topic | fractional order multiagent systems (FOMASs) distributed optimization dynamic surface control (DSC) neural networks observer |
| url | https://www.mdpi.com/2504-3110/6/11/642 |
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