Recursive Learning-Based Bilinear Subspace Identification for Online Modeling and Predictive Control of a Complicated Industrial Process

Recursive Learning-Based Bilinear Subspace Identification for Online Modeling and Predictive Control of a Complicated Industrial Process

In this paper, a recursive learning based bilinear subspace identification (R-B-SI) algorithm is proposed for online modeling and data-driven predictive control of blast furnace (BF) ironmaking process with strong nonlinear time-varying dynamics. Different from the existing linear SI algorithms, the...

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Bibliographic Details
Main Authors: Ping Zhou, Shuai Zhang, Peng Dai
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Recursive bilinear subspace identification
online modeling
recursive learning
data-driven predictive control
blast furnace ironmaking process
nonlinear time-varying dynamics
Online Access:https://ieeexplore.ieee.org/document/9050777/

Internet

https://ieeexplore.ieee.org/document/9050777/

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