Further Results on Adaptive Stabilization of High-Order Stochastic Nonlinear Systems Subject to Uncertainties
This paper concerns the adaptive state-feedback control for a class of high-order stochastic nonlinear systems with uncertainties including time-varying delay, unknown control gain, and parameter perturbation. The commonly used growth assumptions on system nonlinearities are removed, and the adaptiv...
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| Published in | IEEE transaction on neural networks and learning systems Vol. 31; no. 1; pp. 225 - 234 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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United States
IEEE
01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | This paper concerns the adaptive state-feedback control for a class of high-order stochastic nonlinear systems with uncertainties including time-varying delay, unknown control gain, and parameter perturbation. The commonly used growth assumptions on system nonlinearities are removed, and the adaptive control technique is combined with the sign function to deal with the unknown control gain. Then, with the help of the radial basis function neural network approximation approach and Lyapunov-Krasovskii functional, an adaptive state-feedback controller is obtained through the backstepping design procedure. It is verified that the constructed controller can render the closed-loop system semiglobally uniformly ultimately bounded. Finally, both the practical and numerical examples are presented to validate the effectiveness of the proposed scheme. |
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| AbstractList | This paper concerns the adaptive state-feedback control for a class of high-order stochastic nonlinear systems with uncertainties including time-varying delay, unknown control gain, and parameter perturbation. The commonly used growth assumptions on system nonlinearities are removed, and the adaptive control technique is combined with the sign function to deal with the unknown control gain. Then, with the help of the radial basis function neural network approximation approach and Lyapunov-Krasovskii functional, an adaptive state-feedback controller is obtained through the backstepping design procedure. It is verified that the constructed controller can render the closed-loop system semiglobally uniformly ultimately bounded. Finally, both the practical and numerical examples are presented to validate the effectiveness of the proposed scheme. This paper concerns the adaptive state-feedback control for a class of high-order stochastic nonlinear systems with uncertainties including time-varying delay, unknown control gain, and parameter perturbation. The commonly used growth assumptions on system nonlinearities are removed, and the adaptive control technique is combined with the sign function to deal with the unknown control gain. Then, with the help of the radial basis function neural network approximation approach and Lyapunov-Krasovskii functional, an adaptive state-feedback controller is obtained through the backstepping design procedure. It is verified that the constructed controller can render the closed-loop system semiglobally uniformly ultimately bounded. Finally, both the practical and numerical examples are presented to validate the effectiveness of the proposed scheme.This paper concerns the adaptive state-feedback control for a class of high-order stochastic nonlinear systems with uncertainties including time-varying delay, unknown control gain, and parameter perturbation. The commonly used growth assumptions on system nonlinearities are removed, and the adaptive control technique is combined with the sign function to deal with the unknown control gain. Then, with the help of the radial basis function neural network approximation approach and Lyapunov-Krasovskii functional, an adaptive state-feedback controller is obtained through the backstepping design procedure. It is verified that the constructed controller can render the closed-loop system semiglobally uniformly ultimately bounded. Finally, both the practical and numerical examples are presented to validate the effectiveness of the proposed scheme. |
| Author | Min, Huifang Xu, Shengyuan Zhang, Zhengqiang Gu, Jason Zhang, Baoyong |
| Author_xml | – sequence: 1 givenname: Huifang orcidid: 0000-0002-8015-9649 surname: Min fullname: Min, Huifang email: jiejie1043640772@126.com organization: School of Automation, Nanjing University of Science and Technology, Nanjing, China – sequence: 2 givenname: Shengyuan orcidid: 0000-0002-3015-0662 surname: Xu fullname: Xu, Shengyuan email: syxu@njust.edu.cn organization: School of Automation, Nanjing University of Science and Technology, Nanjing, China – sequence: 3 givenname: Jason orcidid: 0000-0002-7626-1077 surname: Gu fullname: Gu, Jason email: jason.gu@dal.ca organization: Department of Electrical and Computer Engineering, Dalhousie University, Halifax, Canada – sequence: 4 givenname: Baoyong orcidid: 0000-0001-5271-2462 surname: Zhang fullname: Zhang, Baoyong email: baoyongzhang@njust.edu.cn organization: School of Automation, Nanjing University of Science and Technology, Nanjing, China – sequence: 5 givenname: Zhengqiang orcidid: 0000-0002-7163-7709 surname: Zhang fullname: Zhang, Zhengqiang email: qufuzzq@126.com organization: School of Engineering, Qufu Normal University, Rizhao, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30908242$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Adaptive control Adaptive systems Artificial neural networks Control systems Delays Feedback Feedback control high-order stochastic nonlinear systems Neural networks Nonlinear systems Parameter uncertainty Perturbation Radial basis function radial basis function neural network (RBF NN) Stochastic processes Stochastic systems Stochasticity time delay Uncertainty unknown control gain |
| Title | Further Results on Adaptive Stabilization of High-Order Stochastic Nonlinear Systems Subject to Uncertainties |
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