Plant–Model Mismatch Estimation from Closed-Loop Data for State-Space Model Predictive Control¶,Industrial & Engineering Chemistry Research

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Plant–Model Mismatch Estimation from Closed-Loop Data for State-Space Model Predictive Control¶
Industrial & Engineering Chemistry Research ( IF 3.8 ) Pub Date : 2018-03-05 00:00:00 , DOI: 10.1021/acs.iecr.7b04917
Jodie M. Simkoff ₁ , Siyun Wang ₁ , Michael Baldea ₁ , Leo H. Chiang ₂ , Ivan Castillo ₂ , Rahul Bindlish ₂ , David B. Stanley ₂

Affiliation

The area of controller performance monitoring, assessment, and diagnosis for model predictive control (MPC) has seen an increase in interest in recent years. A frequently identified cause of degraded performance is mismatch between the plant model used in the controller and the true dynamics of the plant. Most recent research focuses on locating plant–model mismatch in order to reduce the considerable effort required to re-identify the model. In this paper, we present a novel autocovariance-based plant–model mismatch estimation approach for unconstrained MPC based on linear state space models. We show that (additive) plant–model mismatch can be quantified as the solution of an optimization problem which minimizes the discrepancy between the sample autocovariance of plant outputs and the corresponding value obtained from theoretical predictions. We illustrate our theoretical results with two simulation case studies, demonstrating good performance in estimating parametric mismatch.

中文翻译：

植物模型失配估计从闭环数据的状态空间模型预测控制¶

近年来，对用于模型预测控制（MPC）的控制器性能监视，评估和诊断领域的兴趣有所增加。导致性能下降的一个常见原因是控制器中使用的工厂模型与工厂的真实动态不匹配。最近的研究集中于定位工厂模型不匹配，以减少重新识别模型所需的大量工作。在本文中，我们提出了一种基于线性状态空间模型的无约束MPC的基于自协方差的新型工厂模型失配估计方法。我们表明，（附加）工厂模型不匹配可以作为优化问题的解决方案进行量化，该问题可以使工厂产出的样本自协方差与从理论预测中获得的相应值之间的差异最小化。

更新日期：2018-03-06

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