Data-driven Generalized Minimum Variance Regulatory Control Using Routine Operation Data
Data-driven Generalized Minimum Variance Regulatory Control Using Routine Operation Data
复制标题
使用常规操作数据的数据驱动的广义最小方差监管控制
DOI：
10.1002/asjc.2776
复制
发表时间：
2022
影响因子：
2.4
通讯作者：
R. Uematsu and S. Masuda
中科院分区：
文献类型：
--
作者：
R. Uematsu;S. Masuda;and M. Kano;R. Uematsu and S. Masuda
本文提出了一种新的广义最小方差（GMV）控制方法。该方法不需要额外的实验和参考模型就可以实现干扰抑制，这与其他数据驱动技术不同。本文针对Box和Jenkins（BJ）模型提出了一种新的数据驱动准则，这是一种更一般的描述，包括自回归和移动平均异源（ARMAX）模型。证明了对所提判据的优化可以实现GMV控制。两种不同模型结构的数值算例表明了该方法的有效性。特别是，从连续搅拌釜式反应器（CSTR）获得的数据集的应用程序证明了所提出的方法的效率。
This paper provides a new generalized minimum variance (GMV) control using routine operation data. The proposed method achieves disturbance rejection without additional experiments and reference models, which is different from other data‐driven techniques. In this paper, a new data‐driven criterion is proposed for the Box and Jenkins (BJ) model, which is a more general description including the Auto‐Regressive and Moving Average eXogeneous (ARMAX) model. The paper proves that the optimization of the proposed criterion can achieve GMV control. Numerical examples for two different model structures show the validity of the proposed method. In particular, the application to datasets obtained from a continuous stirred tank reactor (CSTR) demonstrates the efficiency of the proposed method.