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An Approximate Control Algorithm for Zero-Sum Differential Games Using Adaptive Critic Technique

机译:基于自适应批评技术的零和微分博弈的近似控制算法

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This paper investigates the optimal control for a class of continuous-time systems in the framework of zero-sum differential game. A novel adaptive dynamic programming (ADP) algorithm is proposed to approximate the optimal value function through a critic neural network (NN). First, a class of control systems with external disturbances are formulated as two-player zero-sum differential game. Then, the proposed approximate structure is implemented to obtain the approximate optimal control policy and the worst-base disturbance policy. Moreover, Lyapunov theory is utilized to demonstrate the uniform ultimate bounded stability of the closed-loop system. Finally, two simulation examples are given to demonstrate the effectiveness of the developed control scheme.
机译:在零和微分博弈框架下,研究了一类连续时间系统的最优控制。提出了一种新颖的自适应动态规划(ADP)算法,通过批评者神经网络(NN)逼近最优值函数。首先,将一类具有外部干扰的控制系统表述为两人零和微分博弈。然后,实施所提出的近似结构以获得近似最优控制策略和最差基极干扰策略。此外,利用李雅普诺夫理论证明了闭环系统的一致极限有界稳定性。最后,给出了两个仿真例子来说明所开发控制方案的有效性。

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