Nonzero-sum games using actor-critic neural networks: A dynamic event-triggered adaptive dynamic programming
This paper mainly investigates the nonzero-sum games of nonlinear systems with unmatched uncertainty by using actor-critic neural networks. To handle the unmatched components, an auxiliary system with a modified value function is constructed, which transforms the robust stabilization issue into the...
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| Veröffentlicht in: | Information sciences Jg. 662; S. 120236 |
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| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Inc
01.03.2024
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| Schlagworte: | |
| ISSN: | 0020-0255, 1872-6291 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | This paper mainly investigates the nonzero-sum games of nonlinear systems with unmatched uncertainty by using actor-critic neural networks. To handle the unmatched components, an auxiliary system with a modified value function is constructed, which transforms the robust stabilization issue into the optimal control issue. Then, a novel dynamic event-triggering condition is designed to further save bandwidth via introducing a dynamic variable. In addition, the actor-critic algorithm is employed in adaptive dynamic programming to achieve Nash equilibrium, which is tuned together with the control policy. By constructing appropriate Lyapunov functions, a criterion is established to ensure that the considered system is uniformly ultimately bounded. Finally, the effectiveness of the developed strategy is demonstrated by an example. |
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| ISSN: | 0020-0255 1872-6291 |
| DOI: | 10.1016/j.ins.2024.120236 |