An Improved Partial-Form MFAC Design for Discrete-Time Nonlinear Systems With Neural Networks
This article investigates the partial-form model-free adaptive control (MFAC) issue for a class of discrete-time nonlinear systems. An improved partial-form MFAC design named IPFMFAC-NN is proposed, where neural networks are introduced to enhance the control performance. With the excellent approxima...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9374966/ |
| _version_ | 1818727585483325440 |
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| author | Ye Yang Chen Chen Jiangang Lu |
| author_facet | Ye Yang Chen Chen Jiangang Lu |
| author_sort | Ye Yang |
| collection | DOAJ |
| description | This article investigates the partial-form model-free adaptive control (MFAC) issue for a class of discrete-time nonlinear systems. An improved partial-form MFAC design named IPFMFAC-NN is proposed, where neural networks are introduced to enhance the control performance. With the excellent approximation ability of radial basis function (RBF) neural networks, the pseudo gradient (PG) values of control method can be directly approximated online using the measured input and output data of the controlled system. Besides, long short-term memory (LSTM) neural networks are used to tune the essential parameters of the control method online with system error set and gradient information set. Finally, the effectiveness and applicability are verified by SISO discrete nonlinear system simulation and three-tank system simulation, and experimental results demonstrate that the proposed method achieves the best control performance in all five indices. Especially compared with the partial-form MFAC, the proposed method reduces the <inline-formula> <tex-math notation="LaTeX">$RMSE$ </tex-math></inline-formula> index by 43.83% and 6.39%, respectively in two simulations, making it a promising control method for discrete-time nonlinear systems. |
| first_indexed | 2024-12-17T22:16:26Z |
| format | Article |
| id | doaj.art-044c9a60a466486ca6c907763cb4d453 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T22:16:26Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-044c9a60a466486ca6c907763cb4d4532022-12-21T21:30:35ZengIEEEIEEE Access2169-35362021-01-019414414145510.1109/ACCESS.2021.30653119374966An Improved Partial-Form MFAC Design for Discrete-Time Nonlinear Systems With Neural NetworksYe Yang0https://orcid.org/0000-0002-1165-1314Chen Chen1https://orcid.org/0000-0002-2595-7816Jiangang Lu2https://orcid.org/0000-0002-1551-6179State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, ChinaState Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, ChinaState Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, ChinaThis article investigates the partial-form model-free adaptive control (MFAC) issue for a class of discrete-time nonlinear systems. An improved partial-form MFAC design named IPFMFAC-NN is proposed, where neural networks are introduced to enhance the control performance. With the excellent approximation ability of radial basis function (RBF) neural networks, the pseudo gradient (PG) values of control method can be directly approximated online using the measured input and output data of the controlled system. Besides, long short-term memory (LSTM) neural networks are used to tune the essential parameters of the control method online with system error set and gradient information set. Finally, the effectiveness and applicability are verified by SISO discrete nonlinear system simulation and three-tank system simulation, and experimental results demonstrate that the proposed method achieves the best control performance in all five indices. Especially compared with the partial-form MFAC, the proposed method reduces the <inline-formula> <tex-math notation="LaTeX">$RMSE$ </tex-math></inline-formula> index by 43.83% and 6.39%, respectively in two simulations, making it a promising control method for discrete-time nonlinear systems.https://ieeexplore.ieee.org/document/9374966/LSTM neural networkspartial-form model-free adaptive controllerRBF neural networksthree-tank system |
| spellingShingle | Ye Yang Chen Chen Jiangang Lu An Improved Partial-Form MFAC Design for Discrete-Time Nonlinear Systems With Neural Networks IEEE Access LSTM neural networks partial-form model-free adaptive controller RBF neural networks three-tank system |
| title | An Improved Partial-Form MFAC Design for Discrete-Time Nonlinear Systems With Neural Networks |
| title_full | An Improved Partial-Form MFAC Design for Discrete-Time Nonlinear Systems With Neural Networks |
| title_fullStr | An Improved Partial-Form MFAC Design for Discrete-Time Nonlinear Systems With Neural Networks |
| title_full_unstemmed | An Improved Partial-Form MFAC Design for Discrete-Time Nonlinear Systems With Neural Networks |
| title_short | An Improved Partial-Form MFAC Design for Discrete-Time Nonlinear Systems With Neural Networks |
| title_sort | improved partial form mfac design for discrete time nonlinear systems with neural networks |
| topic | LSTM neural networks partial-form model-free adaptive controller RBF neural networks three-tank system |
| url | https://ieeexplore.ieee.org/document/9374966/ |
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