Design of extended Kalman filtering neural network control system based on particle swarm identification of nonlinear U-model
This paper studies the modelling of a class of nonlinear plants with known structures but unknown parameters and proposes a general nonlinear U-model expression. The particle swarm optimization algorithm is used to identify the time-varying parameters of the nonlinear U-model online, which solves th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-07-01
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| Series: | Automatika |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2022.2052398 |
| _version_ | 1818059618786476032 |
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| author | Fengxia Xu Xinyu Zhang Zhongda Lu Shanshan Wang |
| author_facet | Fengxia Xu Xinyu Zhang Zhongda Lu Shanshan Wang |
| author_sort | Fengxia Xu |
| collection | DOAJ |
| description | This paper studies the modelling of a class of nonlinear plants with known structures but unknown parameters and proposes a general nonlinear U-model expression. The particle swarm optimization algorithm is used to identify the time-varying parameters of the nonlinear U-model online, which solves the identification problem of the nonlinear U-model system. Newton iterative algorithm is used for nonlinear model transformation. Extended Kalman filter (EKF) is used as the learning algorithm of radial basis function (RBF) neural network to solve the interference problem in a nonlinear system. After determining the number of network nodes in the neural network, EKF can simultaneously determine the network threshold and weight matrix, use the online learning ability of the neural network, adjust the network parameters, make the system output track the ideal output, and improve the convergence speed and anti-noise capability of the system. Finally, simulation examples are used to verify the identification effect of the particle swarm identification algorithm based on the U-model and the effectiveness of the extended Kalman filtering neural network control system based on particle swarm identification. |
| first_indexed | 2024-12-10T13:19:24Z |
| format | Article |
| id | doaj.art-c1bfdff4a9c942d2a93a4320ad0e426c |
| institution | Directory Open Access Journal |
| issn | 0005-1144 1848-3380 |
| language | English |
| last_indexed | 2024-12-10T13:19:24Z |
| publishDate | 2022-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj.art-c1bfdff4a9c942d2a93a4320ad0e426c2022-12-22T01:47:24ZengTaylor & Francis GroupAutomatika0005-11441848-33802022-07-0163346347310.1080/00051144.2022.2052398Design of extended Kalman filtering neural network control system based on particle swarm identification of nonlinear U-modelFengxia Xu0Xinyu Zhang1Zhongda Lu2Shanshan Wang3College of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar, People’s Republic of ChinaCollege of Computer and Control Engineering, Qiqihar University, Qiqihar, People’s Republic of ChinaCollege of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar, People’s Republic of ChinaCollege of Computer and Control Engineering, Qiqihar University, Qiqihar, People’s Republic of ChinaThis paper studies the modelling of a class of nonlinear plants with known structures but unknown parameters and proposes a general nonlinear U-model expression. The particle swarm optimization algorithm is used to identify the time-varying parameters of the nonlinear U-model online, which solves the identification problem of the nonlinear U-model system. Newton iterative algorithm is used for nonlinear model transformation. Extended Kalman filter (EKF) is used as the learning algorithm of radial basis function (RBF) neural network to solve the interference problem in a nonlinear system. After determining the number of network nodes in the neural network, EKF can simultaneously determine the network threshold and weight matrix, use the online learning ability of the neural network, adjust the network parameters, make the system output track the ideal output, and improve the convergence speed and anti-noise capability of the system. Finally, simulation examples are used to verify the identification effect of the particle swarm identification algorithm based on the U-model and the effectiveness of the extended Kalman filtering neural network control system based on particle swarm identification.https://www.tandfonline.com/doi/10.1080/00051144.2022.2052398U-modelparticle swarm identificationextended Kalman filteringneural network control |
| spellingShingle | Fengxia Xu Xinyu Zhang Zhongda Lu Shanshan Wang Design of extended Kalman filtering neural network control system based on particle swarm identification of nonlinear U-model Automatika U-model particle swarm identification extended Kalman filtering neural network control |
| title | Design of extended Kalman filtering neural network control system based on particle swarm identification of nonlinear U-model |
| title_full | Design of extended Kalman filtering neural network control system based on particle swarm identification of nonlinear U-model |
| title_fullStr | Design of extended Kalman filtering neural network control system based on particle swarm identification of nonlinear U-model |
| title_full_unstemmed | Design of extended Kalman filtering neural network control system based on particle swarm identification of nonlinear U-model |
| title_short | Design of extended Kalman filtering neural network control system based on particle swarm identification of nonlinear U-model |
| title_sort | design of extended kalman filtering neural network control system based on particle swarm identification of nonlinear u model |
| topic | U-model particle swarm identification extended Kalman filtering neural network control |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2022.2052398 |
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