Control architectures and autonomy stacks for complex engineering systems are often divided into layers to decompose a complex problem and solution into distinct, manageable sub-problems. To simplify designs, uncertainties are often ignored across layers, an approach with deep roots in classical notions of separation and certainty equivalence. But to develop robust architectures, especially as interactions between data-driven learning layers and model-based decision-making layers grow more intricate, more sophisticated interfaces between layers are required. We propose a basic architecture that couples a statistical parameter estimation layer with a constrained optimization layer. We show how the layers can be tightly integrated by combining bootstrap resampling with distributionally robust optimization. The approach allows a finite-data out-of-sample safety guarantee and an exact reformulation as a tractable finite-dimensional convex optimization problem.
翻译:复杂工程系统的控制架构和自主堆叠往往被分成几层,将复杂问题和解决方案分解为独特、可管理的子问题。为了简化设计,不确定性往往被跨层忽略,这是传统分离和确定性等同概念中根深蒂固的一种方法。但为了发展稳健的架构,特别是当数据驱动的学习层与模型决策层之间的互动日益复杂,需要各层之间更复杂的界面。我们提议了一个基本架构,将统计参数估计层与有限的优化层结合起来。我们展示了如何通过将靴套重新采样与分布强力优化相结合来密切整合这些层。这种方法允许有一定数量的数据外的安全保障和精确的重塑,作为可移植的有限维度组合优化问题。