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Zero-Sum Game Optimized Control With Augmented Time-Synchronized Property
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DOI：10.1109/tac.2026.3676685.png)
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Abstract
En 中文
This article proposes and rigorously develops a reinforcement learning (RL)-based optimized control strategy with notable time-synchronized stability properties for zero-sum differential games. The proposed method addresses the challenge of approximating the time-synchronized Nash equilibrium solutions in nonlinear systems governed by the Hamilton–Jacobi–Isaacs equation. By incorporating a norm-normalized sign function into the learning framework, the system ensures all state-variables converge simultaneously, improving robustness, and energy efficiency. The RL-based optimization iteratively refines the control policies while maintaining system stability underpinned rigorously by Lyapunov-based analysis. To demonstrate the effectiveness of the proposed approach, a motion control problem for an autonomous vehicle system is simulated, comparing the results with alternative existing fixed-time sliding control and time-synchronized optimized control methods. The results illustrate that the proposed control strategy enhances convergence speed, smoothness, and disturbance rejection, making it well-suited for the requirements of real-world high-precision control applications.
Keywords:
Adaptive dynamic programming
Nash equilibrium
time-synchronized stability (TSS)
zero-sum game
Journal
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7
Papers:
1.3W
Citations:
6.7W
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