Adaptive neural network control for uncertain dual switching nonlinear systems
Abstract Dual switching system is a special hybrid system that contains both deterministic and stochastic switching subsystems. Due to its complex switching mechanism, few studies have been conducted for dual switching systems, especially for systems with uncertainty. Usually, the stochastic subsyst...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-21049-y |
| _version_ | 1811195009955790848 |
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| author | Qianqian Mu Fei Long Lipo Mo Liang Liu |
| author_facet | Qianqian Mu Fei Long Lipo Mo Liang Liu |
| author_sort | Qianqian Mu |
| collection | DOAJ |
| description | Abstract Dual switching system is a special hybrid system that contains both deterministic and stochastic switching subsystems. Due to its complex switching mechanism, few studies have been conducted for dual switching systems, especially for systems with uncertainty. Usually, the stochastic subsystems are described as Markov jump systems. Based upon the upstanding identity of RBF neural network on approaching nonlinear data, the tracking models for uncertain subsystems are constructed and the neural network adaptive controller is designed. The global asymptotic stability almost surely (GAS a.s.) and almost surely exponential stability (ES a.s.) of dual switching nonlinear error systems are investigated by using the energy attenuation theory and Lyapunov function method. An uncertain dual switching system with two subsystems, each with two modes, is studied. The uncertain functions of the subsystems are approximated well, and the approximation error is controlled to be below 0.05. Under the control of the designed adaptive controller and switching rules, the error system can obtain a good convergence rate. The tracking error is quite small compared with the original uncertain dual switching system. |
| first_indexed | 2024-04-12T00:36:41Z |
| format | Article |
| id | doaj.art-b219c968452d4da397bf059f49f18dc8 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T00:36:41Z |
| publishDate | 2022-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b219c968452d4da397bf059f49f18dc82022-12-22T03:55:09ZengNature PortfolioScientific Reports2045-23222022-10-0112111110.1038/s41598-022-21049-yAdaptive neural network control for uncertain dual switching nonlinear systemsQianqian Mu0Fei Long1Lipo Mo2Liang Liu3College of Big Data and Information Engineering, Guizhou UniversitySchool of Artificial Intelligence and Electrical Engineering, Guizhou Institute of TechnologySchool of Mathematics and Statistics, Beijing Technology and Business UniversityCollege of Big Data and Information Engineering, Guizhou UniversityAbstract Dual switching system is a special hybrid system that contains both deterministic and stochastic switching subsystems. Due to its complex switching mechanism, few studies have been conducted for dual switching systems, especially for systems with uncertainty. Usually, the stochastic subsystems are described as Markov jump systems. Based upon the upstanding identity of RBF neural network on approaching nonlinear data, the tracking models for uncertain subsystems are constructed and the neural network adaptive controller is designed. The global asymptotic stability almost surely (GAS a.s.) and almost surely exponential stability (ES a.s.) of dual switching nonlinear error systems are investigated by using the energy attenuation theory and Lyapunov function method. An uncertain dual switching system with two subsystems, each with two modes, is studied. The uncertain functions of the subsystems are approximated well, and the approximation error is controlled to be below 0.05. Under the control of the designed adaptive controller and switching rules, the error system can obtain a good convergence rate. The tracking error is quite small compared with the original uncertain dual switching system.https://doi.org/10.1038/s41598-022-21049-y |
| spellingShingle | Qianqian Mu Fei Long Lipo Mo Liang Liu Adaptive neural network control for uncertain dual switching nonlinear systems Scientific Reports |
| title | Adaptive neural network control for uncertain dual switching nonlinear systems |
| title_full | Adaptive neural network control for uncertain dual switching nonlinear systems |
| title_fullStr | Adaptive neural network control for uncertain dual switching nonlinear systems |
| title_full_unstemmed | Adaptive neural network control for uncertain dual switching nonlinear systems |
| title_short | Adaptive neural network control for uncertain dual switching nonlinear systems |
| title_sort | adaptive neural network control for uncertain dual switching nonlinear systems |
| url | https://doi.org/10.1038/s41598-022-21049-y |
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