论文标题
模型预测控制策略近似的基于灵敏度的数据增强框架
A Sensitivity-based Data Augmentation Framework for Model Predictive Control Policy Approximation
论文作者
论文摘要
使用基于专家的监督学习技术近似模型预测控制（MPC）策略需要从MPC策略中采样的标记培训数据集。这通常是通过对可行状态空间进行采样并通过解决每个样本的数值优化问题来评估控制定律来获得的。尽管可以在线评估所产生的近似政策，但生成大型培训样品以了解MPC政策可能会很耗时且过于昂贵。这是限制MPC政策近似设计和实施的基本瓶颈之一。该技术说明旨在应对这一挑战，并提出一种基于直接策略近似的新型基于灵敏度的数据增强方案。所提出的方法是基于利用参数敏感性在现有样品附近生成其他培训样本的廉价。
Approximating model predictive control (MPC) policy using expert-based supervised learning techniques requires labeled training data sets sampled from the MPC policy. This is typically obtained by sampling the feasible state-space and evaluating the control law by solving the numerical optimization problem offline for each sample. Although the resulting approximate policy can be cheaply evaluated online, generating large training samples to learn the MPC policy can be time consuming and prohibitively expensive. This is one of the fundamental bottlenecks that limit the design and implementation of MPC policy approximation. This technical note aims to address this challenge, and proposes a novel sensitivity-based data augmentation scheme for direct policy approximation. The proposed approach is based on exploiting the parametric sensitivities to cheaply generate additional training samples in the neighborhood of the existing samples.
