Adaptive Neural Output Feedback Control of Output-Constrained Nonlinear Systems With Unknown Output Nonlinearity
This paper addresses the problem of adaptive neural output-feedback control for a class of special nonlinear systems with the hysteretic output mechanism and the unmeasured states. A modified Bouc-Wen model is first employed to capture the output hysteresis phenomenon in the design procedure. For it...
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| Published in | IEEE transaction on neural networks and learning systems Vol. 26; no. 8; pp. 1789 - 1802 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.08.2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2162-237X 2162-2388 |
| DOI | 10.1109/TNNLS.2015.2420661 |
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| Abstract | This paper addresses the problem of adaptive neural output-feedback control for a class of special nonlinear systems with the hysteretic output mechanism and the unmeasured states. A modified Bouc-Wen model is first employed to capture the output hysteresis phenomenon in the design procedure. For its fusion with the neural networks and the Nussbaum-type function, two key lemmas are established using some extended properties of this model. To avoid the bad system performance caused by the output nonlinearity, a barrier Lyapunov function technique is introduced to guarantee the prescribed constraint of the tracking error. In addition, a robust filtering method is designed to cancel the restriction that all the system states require to be measured. Based on the Lyapunov synthesis, a new neural adaptive controller is constructed to guarantee the prescribed convergence of the tracking error and the semiglobal uniform ultimate boundedness of all the signals in the closed-loop system. Simulations are implemented to evaluate the performance of the proposed neural control algorithm in this paper. |
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| AbstractList | This paper addresses the problem of adaptive neural output-feedback control for a class of special nonlinear systems with the hysteretic output mechanism and the unmeasured states. A modified Bouc-Wen model is first employed to capture the output hysteresis phenomenon in the design procedure. For its fusion with the neural networks and the Nussbaum-type function, two key lemmas are established using some extended properties of this model. To avoid the bad system performance caused by the output nonlinearity, a barrier Lyapunov function technique is introduced to guarantee the prescribed constraint of the tracking error. In addition, a robust filtering method is designed to cancel the restriction that all the system states require to be measured. Based on the Lyapunov synthesis, a new neural adaptive controller is constructed to guarantee the prescribed convergence of the tracking error and the semiglobal uniform ultimate boundedness of all the signals in the closed-loop system. Simulations are implemented to evaluate the performance of the proposed neural control algorithm in this paper. |
| Author | Zhi Liu Guanyu Lai Chen, Chun Lung Philip Yun Zhang |
| Author_xml | – sequence: 1 surname: Zhi Liu fullname: Zhi Liu email: lz@gdut.edu.cn organization: Fac. of Autom., Guangdong Univ. of Technol., Guangzhou, China – sequence: 2 surname: Guanyu Lai fullname: Guanyu Lai email: lgy124@foxmail.com organization: Fac. of Autom., Guangdong Univ. of Technol., Guangzhou, China – sequence: 3 surname: Yun Zhang fullname: Yun Zhang email: yz@gdut.edu.cn organization: Fac. of Autom., Guangdong Univ. of Technol., Guangzhou, China – sequence: 4 givenname: Chun Lung Philip surname: Chen fullname: Chen, Chun Lung Philip email: philip.chen@ieee.org organization: Fac. of Sci. & Technol., Univ. of Macau, Macau, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25915964$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Adaptation models Adaptive control Adaptive systems Algorithms Artificial neural networks barrier Lyapunov function (BLF) Bouc-Wen hysteresis model Closed loop systems Computer Simulation Feedback Hysteresis Magnetic hysteresis Neural Networks (Computer) neural networks (NNs) Nonlinear Dynamics Nonlinear systems Research Design - statistics & numerical data |
| Title | Adaptive Neural Output Feedback Control of Output-Constrained Nonlinear Systems With Unknown Output Nonlinearity |
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