Adaptive approximation-based design mechanism for non-strict-feedback nonlinear MIMO systems with application to continuous stirred tank reactor
This article is concerned with the problem of adaptive neural controller design for multi-input/multi-output nonlinear systems with input-saturations and disturbances. In the proposed design mechanism, we will take advantage of hyperbolic tangent functions to smooth the sharp corners of the input sa...
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| Published in | ISA transactions Vol. 100; pp. 92 - 102 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Ltd
01.05.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0019-0578 1879-2022 1879-2022 |
| DOI | 10.1016/j.isatra.2019.11.028 |
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| Abstract | This article is concerned with the problem of adaptive neural controller design for multi-input/multi-output nonlinear systems with input-saturations and disturbances. In the proposed design mechanism, we will take advantage of hyperbolic tangent functions to smooth the sharp corners of the input saturations and use Young’s inequality to handle the nonlinear terms derived from the deducing process, and meanwhile apply the intelligent algorithm to estimate the unknown nonlinearity via neural networks. Furthermore, the backstepping technique is used to complete the design of the controller and Lyapunov stability theory is employed to show that the whole closed-loop system is semi-global uniformly ultimately bounded and the tracking error is bounded subject to the small neighborhood of the origin. Finally, as a practical application of the researched design scheme, adaptive neural controller for a continuous stirred tank reactor is constructed. |
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| AbstractList | This article is concerned with the problem of adaptive neural controller design for multi-input/multi-output nonlinear systems with input-saturations and disturbances. In the proposed design mechanism, we will take advantage of hyperbolic tangent functions to smooth the sharp corners of the input saturations and use Young's inequality to handle the nonlinear terms derived from the deducing process, and meanwhile apply the intelligent algorithm to estimate the unknown nonlinearity via neural networks. Furthermore, the backstepping technique is used to complete the design of the controller and Lyapunov stability theory is employed to show that the whole closed-loop system is semi-global uniformly ultimately bounded and the tracking error is bounded subject to the small neighborhood of the origin. Finally, as a practical application of the researched design scheme, adaptive neural controller for a continuous stirred tank reactor is constructed.This article is concerned with the problem of adaptive neural controller design for multi-input/multi-output nonlinear systems with input-saturations and disturbances. In the proposed design mechanism, we will take advantage of hyperbolic tangent functions to smooth the sharp corners of the input saturations and use Young's inequality to handle the nonlinear terms derived from the deducing process, and meanwhile apply the intelligent algorithm to estimate the unknown nonlinearity via neural networks. Furthermore, the backstepping technique is used to complete the design of the controller and Lyapunov stability theory is employed to show that the whole closed-loop system is semi-global uniformly ultimately bounded and the tracking error is bounded subject to the small neighborhood of the origin. Finally, as a practical application of the researched design scheme, adaptive neural controller for a continuous stirred tank reactor is constructed. This article is concerned with the problem of adaptive neural controller design for multi-input/multi-output nonlinear systems with input-saturations and disturbances. In the proposed design mechanism, we will take advantage of hyperbolic tangent functions to smooth the sharp corners of the input saturations and use Young's inequality to handle the nonlinear terms derived from the deducing process, and meanwhile apply the intelligent algorithm to estimate the unknown nonlinearity via neural networks. Furthermore, the backstepping technique is used to complete the design of the controller and Lyapunov stability theory is employed to show that the whole closed-loop system is semi-global uniformly ultimately bounded and the tracking error is bounded subject to the small neighborhood of the origin. Finally, as a practical application of the researched design scheme, adaptive neural controller for a continuous stirred tank reactor is constructed. |
| Author | Niu, Ben Wang, Xiaomei Li, Junqing Duan, Peiyong Yang, Dong Xiang, Zhengrong Jiang, Kun |
| Author_xml | – sequence: 1 givenname: Kun surname: Jiang fullname: Jiang, Kun email: jiangkun0909@126.com organization: School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China – sequence: 2 givenname: Ben surname: Niu fullname: Niu, Ben email: niubensdnu@163.com organization: School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China – sequence: 3 givenname: Xiaomei surname: Wang fullname: Wang, Xiaomei email: wlwxmei@163.com organization: School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China – sequence: 4 givenname: Zhengrong surname: Xiang fullname: Xiang, Zhengrong email: xiangzr@mail.njust.edu.cn organization: School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China – sequence: 5 givenname: Junqing surname: Li fullname: Li, Junqing email: lijunqing@lcu-cs.com organization: School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China – sequence: 6 givenname: Peiyong surname: Duan fullname: Duan, Peiyong email: duanpeiyong@sdnu.edu.cn organization: School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China – sequence: 7 givenname: Dong surname: Yang fullname: Yang, Dong email: yangdong850901@126.com organization: School of Engineering, Qufu Normal University, Rizhao 276826, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31818486$$D View this record in MEDLINE/PubMed |
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publication-title: IEEE Trans Autom Control doi: 10.1109/9.100933 |
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