论文标题
近似信息状态，以控制不确定系统的最坏情况
Approximate Information States for Worst-case Control of Uncertain Systems
论文作者
论文摘要
在本文中，我们研究了一个最严重的案例控制问题，其部分观察到的状态。我们考虑了一种非传统公式，在我们的动力学中，我们的动态噪声和干扰是不确定的变量，这些变量在有限集中采用值。在此类问题中，可以使用动态程序（DP）来得出最佳控制策略。可以使用状态的条件范围而不是内存来改善该DP的计算复杂性。我们提供了对信息状态的更一般的定义，该定义足以构建DP而不会丧失最佳性，并证明条件范围是信息状态的一个示例。接下来，我们扩展此概念以定义近似信息状态和近似DP。当使用近似DP来得出控制策略时，我们还限制了最大最佳损失。最后，我们在数字示例中说明了结果。
In this paper, we investigate a worst-case-scenario control problem with a partially observed state. We consider a non-stochastic formulation, where noises and disturbances in our dynamics are uncertain variables which take values in finite sets. In such problems, the optimal control strategy can be derived using a dynamic program (DP) with respect to the memory. The computational complexity of this DP can be improved using a conditional range of the state instead of the memory. We present a more general definition of an information state which is sufficient to construct a DP without loss of optimality, and show that the conditional range is an example of an information state. Next, we extend this notion to define an approximate information state and an approximate DP. We also bound the maximum loss of optimality when using an approximate DP to derive the control strategy. Finally, we illustrate our results in a numerical example.
