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Iterative learning nonzero‐sum game control for incomplete‐information pursuit‐evasion system with uncertainty.

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Title: Iterative learning nonzero‐sum game control for incomplete‐information pursuit‐evasion system with uncertainty.
Authors: Zhang, Peng1 (AUTHOR), Che, Guangyuan1 (AUTHOR), Zheng, Zixuan2 (AUTHOR) z.zheng@nwpu.edu.cn, Zhu, Guangtong2 (AUTHOR)
Source: Asian Journal of Control. Jul2026, Vol. 28 Issue 4, p1946-1958. 13p.
Subjects: Iterative learning control, Uncertainty (Information theory), Feedback control systems, Riccati equation
Abstract: In this article, an iterative learning nonzero‐sum game (NSG) control approach is investigated for incomplete‐information pursuit‐evasion (PE) systems subject to uncertainty. Due to the non‐shared quality of the cost functions, the NSG frame is established for the incomplete‐information PE issue. To deal with the uncertainty, the upper bounds of the cost functions are derived for both pursuer and evader. Then, the suboptimal game control strategies are suggested for the uncertain complete‐information PE game. For the incomplete‐information case, an adaptive learning gain estimator is devised to estimate the control gain of the evader. In the light of this estimation, the incomplete‐information pursuit control policy is proposed to guarantee the asymptotic stability of the whole system including the PE system and the gain estimation error system. Meanwhile, a matrix iterative learning algorithm is proposed to solve the modified Riccati equation. Finally, the simulated outcomes are presented to validate the efficacy of the designed incomplete‐information PE control approaches. [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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  Data: Iterative learning nonzero‐sum game control for incomplete‐information pursuit‐evasion system with uncertainty.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Peng%22">Zhang, Peng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Che%2C+Guangyuan%22">Che, Guangyuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zheng%2C+Zixuan%22">Zheng, Zixuan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> z.zheng@nwpu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Guangtong%22">Zhu, Guangtong</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Asian+Journal+of+Control%22">Asian Journal of Control</searchLink>. Jul2026, Vol. 28 Issue 4, p1946-1958. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Iterative+learning+control%22">Iterative learning control</searchLink><br /><searchLink fieldCode="DE" term="%22Uncertainty+%28Information+theory%29%22">Uncertainty (Information theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+control+systems%22">Feedback control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Riccati+equation%22">Riccati equation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this article, an iterative learning nonzero‐sum game (NSG) control approach is investigated for incomplete‐information pursuit‐evasion (PE) systems subject to uncertainty. Due to the non‐shared quality of the cost functions, the NSG frame is established for the incomplete‐information PE issue. To deal with the uncertainty, the upper bounds of the cost functions are derived for both pursuer and evader. Then, the suboptimal game control strategies are suggested for the uncertain complete‐information PE game. For the incomplete‐information case, an adaptive learning gain estimator is devised to estimate the control gain of the evader. In the light of this estimation, the incomplete‐information pursuit control policy is proposed to guarantee the asymptotic stability of the whole system including the PE system and the gain estimation error system. Meanwhile, a matrix iterative learning algorithm is proposed to solve the modified Riccati equation. Finally, the simulated outcomes are presented to validate the efficacy of the designed incomplete‐information PE control approaches. [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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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1002/asjc.3783
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 13
        StartPage: 1946
    Subjects:
      – SubjectFull: Iterative learning control
        Type: general
      – SubjectFull: Uncertainty (Information theory)
        Type: general
      – SubjectFull: Feedback control systems
        Type: general
      – SubjectFull: Riccati equation
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      – TitleFull: Iterative learning nonzero‐sum game control for incomplete‐information pursuit‐evasion system with uncertainty.
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            NameFull: Zhang, Peng
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            NameFull: Che, Guangyuan
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            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
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              Value: 28
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            – TitleFull: Asian Journal of Control
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