Optimal Time-Varying Q-Learning Algorithm for Affine Nonlinear Systems With Coupled Players
To address the finite-horizon coupled two-player mixed <inline-formula> <tex-math notation="LaTeX">H_{2}/H_{\infty } </tex-math></inline-formula> control challenge within a continuous-time affine nonlinear system, this research introduces a distinctive Q-function an...
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| Vydáno v: | IEEE transactions on systems, man, and cybernetics. Systems Ročník 55; číslo 10; s. 7037 - 7047 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
01.10.2025
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| Témata: | |
| ISSN: | 2168-2216, 2168-2232 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | To address the finite-horizon coupled two-player mixed <inline-formula> <tex-math notation="LaTeX">H_{2}/H_{\infty } </tex-math></inline-formula> control challenge within a continuous-time affine nonlinear system, this research introduces a distinctive Q-function and presents an innovative adaptive dynamic programming (ADP) method that operates autonomously of system-specific information. Initially, we formulate the time-varying Hamilton-Jacobi-Isaacs (HJI) equations, which pose a significant challenge for resolution due to their time-dependent and nonlinear nature. Subsequently, a novel offline policy iteration (PI) algorithm is introduced, highlighting its convergence and reinforcing the substantive proof of the existence of Nash equilibrium points. Moreover, a novel action-dependent Q-function is established to facilitate entirely model-free learning, representing the initial foray into the mixed <inline-formula> <tex-math notation="LaTeX">H_{2}/H_{\infty } </tex-math></inline-formula> control problem involving coupled players. The Lyapunov direct approach is employed to ensure the stability of the closed-loop uncertain affine nonlinear system under the ADP-based control scheme, guaranteeing uniform ultimate boundedness (UUB). Finally, a numerical simulation is conducted to validate the effectiveness of the aforementioned ADP-based control approach. |
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| ISSN: | 2168-2216 2168-2232 |
| DOI: | 10.1109/TSMC.2025.3580988 |