论文标题
在预测控制中，状态空间模型与多步预测指标：状态空间模型是否使安全数据驱动的设计复杂化？
State space models vs. multi-step predictors in predictive control: Are state space models complicating safe data-driven designs?
论文作者
论文摘要
本文将线性预测控制的递归状态空间模型和直接多步预测指标进行了对比。我们为两个模型结构提供了一个教程的说明，以解决以下问题：1。随机最佳控制； 2。系统识别； 3。基于估计模型的随机最佳控制。在整篇文章中，我们提供了有关这两个模型参数的益处和局限性的详细讨论，以进行预测控制，并强调与现有作品的关系。此外，我们得出了一种新颖的（部分紧密）的约束，以拧紧随机预测控制，并在多步预测器中具有参数不确定性。
This paper contrasts recursive state space models and direct multi-step predictors for linear predictive control. We provide a tutorial exposition for both model structures to solve the following problems: 1. stochastic optimal control; 2. system identification; 3. stochastic optimal control based on the estimated model. Throughout the paper, we provide detailed discussions of the benefits and limitations of these two model parametrizations for predictive control and highlight the relation to existing works. Additionally, we derive a novel (partially tight) constraint tightening for stochastic predictive control with parametric uncertainty in the multi-step predictor.
