完全概率控制的灵敏度和安全性
Sensitivity and safety of fully probabilistic control
摘要：
在本文中，我们提出了一个灵敏度分析所谓的全概率控制方案。该方案试图通过一个密度函数（pdf）来控制一个系统，并通过计算一个在Kullback-Leibler意义上最优的概率控制策略来实现。在神经网络、强化学习和数据驱动的迭代控制的背景下，通过pdf对感兴趣的系统进行建模的情况自然会出现。在介绍了灵敏度分析之后，我们重点描述了闭环系统的收敛区域，并介绍了该方案的安全性分析。通过仿真说明了结果。这是题为“关于数据驱动模型不确定性的完全概率控制的鲁棒稳定性”的论文的初步版本，该论文将在2019年欧洲控制会议上发表。
英文摘要：
In this paper we present a sensitivity analysis for the so-called fully probabilistic control scheme. This scheme attempts to control a system modeled via a probability density function (pdf) and does so by computing a probabilistic control policy that is optimal in the Kullback-Leibler sense. Situations where a system of interest is modeled via a pdf naturally arise in the context of neural networks, reinforcement learning and data-driven iterative control. After presenting the sensitivity analysis, we focus on characterizing the convergence region of the closed loop system and introduce a safety analysis for the scheme. The results are illustrated via simulations. This is the preliminary version of the paper entitled "On robust stability of fully probabilistic control with respect to data-driven model uncertainties" that will be presented at the 2019 European Control Conference.
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