A Nonlinear System State Estimation Method Based on Adaptive Fusion of Multiple Kernel Functions
With the development of the industry, the physical model of controlled object tends to be complicated and unknown. It is particularly important to estimate the state variables of a nonlinear system when the model is unknown. This paper proposes a state estimation method based on adaptive fusion of m...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5124841 |
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| _version_ | 1850220671672516608 |
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| author | Daxing Xu Aiyu Hu Xuelong Han Lu Zhang |
| author_facet | Daxing Xu Aiyu Hu Xuelong Han Lu Zhang |
| author_sort | Daxing Xu |
| collection | DOAJ |
| description | With the development of the industry, the physical model of controlled object tends to be complicated and unknown. It is particularly important to estimate the state variables of a nonlinear system when the model is unknown. This paper proposes a state estimation method based on adaptive fusion of multiple kernel functions to improve the accuracy of system state estimation. First, a dynamic neural network is used to build the system state model, where the kernel function node is constructed by a weighted linear combination of multiple local kernel functions and global kernel functions. Then, the state of the system and the weight of the kernel functions are put together to form an augmented state vector, which can be estimated in real time by using high-degree cubature Kalman filter. The high-degree cubature Kalman filter performs adaptive fusion of the kernel function weights according to specific samples, which makes the neural network function approximate the real system model, and the state estimate follows the real value. Finally, the simulation results verify the feasibility and effectiveness of the proposed algorithm. |
| format | Article |
| id | doaj-art-75c27ecadffc4413b60629c8e8942919 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-75c27ecadffc4413b60629c8e89429192025-08-20T02:06:58ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/51248415124841A Nonlinear System State Estimation Method Based on Adaptive Fusion of Multiple Kernel FunctionsDaxing Xu0Aiyu Hu1Xuelong Han2Lu Zhang3College of Electrical and Information Engineering, Quzhou University, Quzhou 324000, ChinaZhejiang Juhua Group Co., Ltd., Quzhou 324000, ChinaCollege of Electrical and Information Engineering, Quzhou University, Quzhou 324000, ChinaCollege of Electrical and Information Engineering, Quzhou University, Quzhou 324000, ChinaWith the development of the industry, the physical model of controlled object tends to be complicated and unknown. It is particularly important to estimate the state variables of a nonlinear system when the model is unknown. This paper proposes a state estimation method based on adaptive fusion of multiple kernel functions to improve the accuracy of system state estimation. First, a dynamic neural network is used to build the system state model, where the kernel function node is constructed by a weighted linear combination of multiple local kernel functions and global kernel functions. Then, the state of the system and the weight of the kernel functions are put together to form an augmented state vector, which can be estimated in real time by using high-degree cubature Kalman filter. The high-degree cubature Kalman filter performs adaptive fusion of the kernel function weights according to specific samples, which makes the neural network function approximate the real system model, and the state estimate follows the real value. Finally, the simulation results verify the feasibility and effectiveness of the proposed algorithm.http://dx.doi.org/10.1155/2021/5124841 |
| spellingShingle | Daxing Xu Aiyu Hu Xuelong Han Lu Zhang A Nonlinear System State Estimation Method Based on Adaptive Fusion of Multiple Kernel Functions Complexity |
| title | A Nonlinear System State Estimation Method Based on Adaptive Fusion of Multiple Kernel Functions |
| title_full | A Nonlinear System State Estimation Method Based on Adaptive Fusion of Multiple Kernel Functions |
| title_fullStr | A Nonlinear System State Estimation Method Based on Adaptive Fusion of Multiple Kernel Functions |
| title_full_unstemmed | A Nonlinear System State Estimation Method Based on Adaptive Fusion of Multiple Kernel Functions |
| title_short | A Nonlinear System State Estimation Method Based on Adaptive Fusion of Multiple Kernel Functions |
| title_sort | nonlinear system state estimation method based on adaptive fusion of multiple kernel functions |
| url | http://dx.doi.org/10.1155/2021/5124841 |
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