论文标题
从闭环操作数据中学习因果稳态模型的调整公式
Adjustment formulas for learning causal steady-state models from closed-loop operational data
论文作者
论文摘要
从历史操作数据中学到的稳态模型可能不适合基于模型的优化，除非训练数据中由控制引入的相关性。利用结构性动力因果模型的工作的最新结果，我们得出了一个用于调整此控制混淆的公式，从而从闭环稳态数据中估算了因果稳态模型的估计。该公式假设已根据一些固定控制法收集了可用数据。它通过估算和考虑控制器试图抵消的干扰来起作用，并从馈电和反馈控制下收集的数据中学习。
Steady-state models which have been learned from historical operational data may be unfit for model-based optimization unless correlations in the training data which are introduced by control are accounted for. Using recent results from work on structural dynamical causal models, we derive a formula for adjusting for this control confounding, enabling the estimation of a causal steady-state model from closed-loop steady-state data. The formula assumes that the available data have been gathered under some fixed control law. It works by estimating and taking into account the disturbance which the controller is trying to counteract, and enables learning from data gathered under both feedforward and feedback control.
