Title: The Bayesian Separation Principle for Data-driven Control Show Chinese title

Title: 数据驱动控制的贝叶斯分离原理

Abstract: This paper investigates the existence of a separation principle between model identification and control design in the context of model predictive control. First, we elucidate that the separation principle holds asymptotically in the number of data in a Fisherian setting, and universally in a Bayesian setting. Then, by formulating model predictive control within a Gaussian regression framework, we describe how the Bayesian separation principle can be used to derive explicit, uncertainty-aware expressions for the control cost and optimal input sequence, thereby bridging direct and indirect data-driven approaches. Show Chinese abstract

Abstract: 本文研究了在模型预测控制背景下，模型识别与控制设计之间的分离原理的存在性。 首先，我们阐明了在费舍尔设定中，分离原理在数据数量上渐近成立，并在贝叶斯设定中普遍成立。 然后，通过在高斯回归框架内表述模型预测控制，我们描述了如何利用贝叶斯分离原理推导出控制成本和最优输入序列的显式、不确定性感知表达式，从而弥合直接和间接的数据驱动方法。