A Robust Controller Design Method Based on Parameter Variation Rate of RBF-ARX Model
As an extension of the exponential autoregressive model and radial basis function (RBF) network, the RBF-ARX model has been widely used in nonlinear system modeling and control. Considering conservativeness of the previous method, which only uses the upper and lower limits of the RBF-ARX model param...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8890793/ |
| _version_ | 1818665704663023616 |
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| author | Feng Zhou Hui Peng Ganglin Zhang Xiaoyong Zeng |
| author_facet | Feng Zhou Hui Peng Ganglin Zhang Xiaoyong Zeng |
| author_sort | Feng Zhou |
| collection | DOAJ |
| description | As an extension of the exponential autoregressive model and radial basis function (RBF) network, the RBF-ARX model has been widely used in nonlinear system modeling and control. Considering conservativeness of the previous method, which only uses the upper and lower limits of the RBF-ARX model parameters to construct a system's polytopic state space model, in this paper, the model's parameter variation rate information is also utilized to compress variation range of the coefficient matrices in the system's state space model. And then, a robust predictive control (RPC) strategy for output tracking without using system's steady state information is designed. The method of constructing the system's polytopic state space model takes advantage of the fact that the RBF-ARX model itself is a special quasi-LPV model, and there is no need to assume the time varying parameters and/or the variation rate of the parameters in the system model are known or measurable. The effectiveness of the proposed control strategy is verified on a continuous stirred tank reactor (CSTR) process. |
| first_indexed | 2024-12-17T05:52:52Z |
| format | Article |
| id | doaj.art-a4f4746293414c63b0ec3b7ccd085e0f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:52:52Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a4f4746293414c63b0ec3b7ccd085e0f2022-12-21T22:01:07ZengIEEEIEEE Access2169-35362019-01-01716028416029410.1109/ACCESS.2019.29513908890793A Robust Controller Design Method Based on Parameter Variation Rate of RBF-ARX ModelFeng Zhou0https://orcid.org/0000-0002-5513-743XHui Peng1Ganglin Zhang2Xiaoyong Zeng3College of Electronic Information and Electrical Engineering, Changsha University, Changsha, ChinaSchool of Automation, Central South University, Changsha, ChinaCollege of Electronic Information and Electrical Engineering, Changsha University, Changsha, ChinaSchool of Automation, Central South University, Changsha, ChinaAs an extension of the exponential autoregressive model and radial basis function (RBF) network, the RBF-ARX model has been widely used in nonlinear system modeling and control. Considering conservativeness of the previous method, which only uses the upper and lower limits of the RBF-ARX model parameters to construct a system's polytopic state space model, in this paper, the model's parameter variation rate information is also utilized to compress variation range of the coefficient matrices in the system's state space model. And then, a robust predictive control (RPC) strategy for output tracking without using system's steady state information is designed. The method of constructing the system's polytopic state space model takes advantage of the fact that the RBF-ARX model itself is a special quasi-LPV model, and there is no need to assume the time varying parameters and/or the variation rate of the parameters in the system model are known or measurable. The effectiveness of the proposed control strategy is verified on a continuous stirred tank reactor (CSTR) process.https://ieeexplore.ieee.org/document/8890793/Robust predictive controlrobustness and stabilitynonlinear modelparameter variation rate |
| spellingShingle | Feng Zhou Hui Peng Ganglin Zhang Xiaoyong Zeng A Robust Controller Design Method Based on Parameter Variation Rate of RBF-ARX Model IEEE Access Robust predictive control robustness and stability nonlinear model parameter variation rate |
| title | A Robust Controller Design Method Based on Parameter Variation Rate of RBF-ARX Model |
| title_full | A Robust Controller Design Method Based on Parameter Variation Rate of RBF-ARX Model |
| title_fullStr | A Robust Controller Design Method Based on Parameter Variation Rate of RBF-ARX Model |
| title_full_unstemmed | A Robust Controller Design Method Based on Parameter Variation Rate of RBF-ARX Model |
| title_short | A Robust Controller Design Method Based on Parameter Variation Rate of RBF-ARX Model |
| title_sort | robust controller design method based on parameter variation rate of rbf arx model |
| topic | Robust predictive control robustness and stability nonlinear model parameter variation rate |
| url | https://ieeexplore.ieee.org/document/8890793/ |
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