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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 15618625 |
| DOI: | 10.1002/asjc.3783 |
