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Recursive Learning Based Bilinear Subspace Identification for Online Modeling and Predictive Control of a Complicated Industrial Process
2020
IEEE Access
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 R-B-SI algorithm can make full use of the process data information by adding the Kronecker product term of input data and the Kronecker product term between input and output data into the data block
doi:10.1109/access.2020.2984319
fatcat:p2r7gmdzofh4zme4wugvf7xrre