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Adaptive neural event‐triggered near‐optimal control for affined uncertain nonlinear discrete‐time system.

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Title: Adaptive neural event‐triggered near‐optimal control for affined uncertain nonlinear discrete‐time system.
Authors: Li, Xinyu1 (AUTHOR), Ding, Liang1 (AUTHOR) liangding@hit.edu.cn, Li, Shu2 (AUTHOR), Yang, Huaiguang1 (AUTHOR), Qi, Huanan1 (AUTHOR), Gao, Haibo1 (AUTHOR), Deng, Zongquan1 (AUTHOR)
Source: Asian Journal of Control. Nov2024, Vol. 26 Issue 6, p3210-3225. 16p.
Subjects: Heuristic programming, Cost functions, Dynamic programming, Nonlinear systems, Algorithms
Abstract: A novel event‐triggered heuristic dynamic programming (HDP) algorithm is proposed for the near‐optimal control of uncertain discrete‐time nonlinear input‐affine systems. Based on input‐to‐state stability (ISS) analysis, a new event‐triggered mechanism (ETM) is designed. Under constant coefficients, a Lipschitz‐like assumption that forms the basis of the event‐triggering condition is considered to be conservative. To further reduce the conservativeness of the triggering condition and enlarge the average interevent time, an adaptive threshold parameter is utilized in the proposed ETM. In the HDP algorithm framework, model, critic, and action network are adopted to achieve state estimation, approximation to the optimal cost function, and solution to Hamilton–Jacobian–Bellman (HJB) equation. Under the proposed event‐triggered HDP algorithm, the closed system is proved to possess semiglobal uniform ultimate boundedness (SGUUB). Finally, by conducting simulation, it shows that on the premise of satisfying control performance, the event‐triggered strategy can realize reduction on the updating frequency of the controller. [ABSTRACT FROM AUTHOR]
Copyright of Asian Journal of Control is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: Adaptive neural event‐triggered near‐optimal control for affined uncertain nonlinear discrete‐time system.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Xinyu%22">Li, Xinyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ding%2C+Liang%22">Ding, Liang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> liangding@hit.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Shu%22">Li, Shu</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Huaiguang%22">Yang, Huaiguang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qi%2C+Huanan%22">Qi, Huanan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gao%2C+Haibo%22">Gao, Haibo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Deng%2C+Zongquan%22">Deng, Zongquan</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Asian+Journal+of+Control%22">Asian Journal of Control</searchLink>. Nov2024, Vol. 26 Issue 6, p3210-3225. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Heuristic+programming%22">Heuristic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Cost+functions%22">Cost functions</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+programming%22">Dynamic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+systems%22">Nonlinear systems</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: A novel event‐triggered heuristic dynamic programming (HDP) algorithm is proposed for the near‐optimal control of uncertain discrete‐time nonlinear input‐affine systems. Based on input‐to‐state stability (ISS) analysis, a new event‐triggered mechanism (ETM) is designed. Under constant coefficients, a Lipschitz‐like assumption that forms the basis of the event‐triggering condition is considered to be conservative. To further reduce the conservativeness of the triggering condition and enlarge the average interevent time, an adaptive threshold parameter is utilized in the proposed ETM. In the HDP algorithm framework, model, critic, and action network are adopted to achieve state estimation, approximation to the optimal cost function, and solution to Hamilton–Jacobian–Bellman (HJB) equation. Under the proposed event‐triggered HDP algorithm, the closed system is proved to possess semiglobal uniform ultimate boundedness (SGUUB). Finally, by conducting simulation, it shows that on the premise of satisfying control performance, the event‐triggered strategy can realize reduction on the updating frequency of the controller. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Asian Journal of Control is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/asjc.3399
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        Text: English
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        PageCount: 16
        StartPage: 3210
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      – SubjectFull: Heuristic programming
        Type: general
      – SubjectFull: Cost functions
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      – SubjectFull: Dynamic programming
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      – SubjectFull: Nonlinear systems
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      – SubjectFull: Algorithms
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      – TitleFull: Adaptive neural event‐triggered near‐optimal control for affined uncertain nonlinear discrete‐time system.
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            NameFull: Li, Xinyu
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            NameFull: Ding, Liang
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            NameFull: Li, Shu
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            – D: 01
              M: 11
              Text: Nov2024
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              Y: 2024
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