Kalman Filter-Based Data-Driven Robust Model-Free Adaptive Predictive Control of a Complicated Industrial Process

The automatic control of blast furnace (BF) ironmaking process has always been an important yet arduous task in metallurgic engineering and automation. In this article, a novel Kalman filter-based robust model-free adaptive predictive control (MFAPC) method is proposed for the direct data-driven con...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on automation science and engineering Vol. 19; no. 2; pp. 788 - 803
Main Authors: Zhou, Ping, Zhang, Shuai, Wen, Liang, Fu, Jun, Chai, Tianyou, Wang, Hong
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation models
Adaptive control
Automatic control
Blast furnace (BF) ironmaking process
Blast furnace practice
Control methods
Control systems design
Controllers
Data integrity
Data loss
direct data-driven control
Iron
Ironmaking
Jacobi matrix method
Jacobian matrix
Kalman filters
Linearization
Loss measurement
Missing data
Multilayers
multiparameter sensitivity analysis
Multivariable control
Multivariate analysis
Noise
Noise measurement
Prediction models
Predictive control
Predictive models
Process control
Quality control
Robust control
robust mechanism
robust model-free adaptive predictive control (MFAPC)
Stability analysis
ISSN:1545-5955, 1558-3783
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The automatic control of blast furnace (BF) ironmaking process has always been an important yet arduous task in metallurgic engineering and automation. In this article, a novel Kalman filter-based robust model-free adaptive predictive control (MFAPC) method is proposed for the direct data-driven control of molten iron quality in BF ironmaking. First, a compact-form dynamic linearization-based extended MFAPC method for multivariable molten iron quality control is proposed by generalizing the existing single-variable MFAPC method to multivariable systems. Based on it, a Kalman filter-based robust MFAPC is further proposed considering the problems of data loss and measurement noise in quality detection. Specifically, the robust mechanism in the robust MFAPC combines a novel dynamic linearization method with a concept termed Pseudo-Jacobian matrix to predict the missing data during data loss. After that, a Kalman filter is constructed based on a prediction model to filter the measurement noise. The stability of the proposed control method is analyzed, and various data experiments using actual industrial data are performed to verify the effectiveness of the proposed methods. Note to Practitioners -The extremely complicated dynamics of blast furnace ironmaking process make the model-based controllers difficult to realize in practice. In this article, a novel robust model-free adaptive predictive control method is proposed for direct data-driven control of multivariate molten iron quality in the ironmaking process. This method directly uses the process input and output data to design the multivariable quality controller online by the compact-form dynamic linearization technology and the internal multilayer prediction mechanism, thus avoids the drawback of model-based controllers in troublesome process modeling. Moreover, the proposed method can effectively avoid the influence of data loss and measurement noise on the controller performance with the designed Kalman filter-based robust mechanism. The superiority and practicability of the proposed method are verified using various experiments against actual industrial data.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1545-5955
1558-3783
DOI:10.1109/TASE.2021.3061974