This paper introduces innovative data-driven techniques for estimating the noise distribution and KL divergence bound for distributionally robust optimal control (DROC). The proposed approach addresses the limitation of traditional DROC approaches that require known ambiguity sets for the noise distribution, our approach can learn these distributions and bounds in real-world scenarios where they may not be known a priori. To evaluate the effectiveness of our approach, a navigation problem involving a car-like robot under different noise distributions is used as a numerical example. The results demonstrate that DROC combined with the proposed data-driven approaches, what we call D3ROC, provide robust and efficient control policies that outperform the traditional iterative linear quadratic Gaussian (iLQG) control approach. Moreover, it shows the effectiveness of our proposed approach in handling different noise distributions. Overall, the proposed approach offers a promising solution to real-world DROC problems where the noise distribution and KL divergence bounds may not be known a priori, increasing the practicality and applicability of the DROC framework.
翻译:本文介绍了用于估计噪音分布和KL差分以进行分配稳健最佳控制的创新数据驱动技术(DROC)。拟议办法处理传统的DROC方法的局限性,这些方法需要已知的噪音分布模棱两可,我们的方法可以在现实情景中了解这些分布和界限,在现实情景中可能无法事先知道这些分布和界限。为了评估我们的方法的有效性,不同噪音分布下涉及汽车类机器人的导航问题被用作数字例子。结果显示DROC与拟议的数据驱动方法(我们称之为D3ROC)相结合,提供了强有力和高效的控制政策,超过了传统的迭代线性象形高斯(iLQG)控制方法。此外,拟议办法显示了我们处理不同噪音分布的拟议方法的有效性。总体而言,拟议办法为现实世界DROC问题提供了一个很有希望的解决办法,因为噪音分布和KL差幅界限可能事先不为人所知,从而增加了DROC框架的实用性和适用性。</s>