In this article, a new data-based adaptive dynamic programming algorithm is proposed to solve the optimal control policy for discrete-time systems with uncertainties. Firstly, for uncertain systems, the corresponding Hamiltonian function is designed, and then the robust adaptive dynamic programming algorithm is obtained. Next, by using the input and output data of the system, the data-based Bellman equation is constructed, and the data-based robust adaptive dynamic programming algorithm is derived, which does not require the accurate model of the system. Finally, a simulation example shows the effectiveness of the proposed algorithm.

中文翻译：

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通过自适应动态规划对具有不确定性的离散时间系统进行基于数据的鲁棒优化控制

在本文中，提出了一种新的基于数据的自适应动态规划算法来求解具有不确定性的离散时间系统的最优控制策略。首先针对不确定系统设计相应的哈密顿函数，进而得到鲁棒自适应动态规划算法。接下来，利用系统的输入和输出数据，构造了基于数据的贝尔曼方程，推导了基于数据的鲁棒自适应动态规划算法，该算法不需要系统的精确模型。最后通过仿真实例说明了所提算法的有效性。