Prior-knowledge-based subspace identification for batch processes. (October 2019)

• Record Type: Journal Article
• Authors: Hou, Jie; Chen, Fengwei; Li, Penghua; Zhu, Zhiqin
• Is Part Of: Journal of process control. Volume 82(2019)
• Page Start: 22
• Page End: 30
• Publication Date: 2019-10
• Subjects: Subspace identification -- Prior knowledge -- Disturbance -- Batch process -- Variance analysis -- Consistent analysis; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2019.07.002
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: A difference operator is proposed to reject the disturbance effect and a new instrumental variable is proposed to eliminate the noise effect for consistent estimation. The new instrumental variable enhances the estimated model efficiency/accuracy, as supported by consistent analysis. The auxiliary static-gain information is used to enhance the accuracy of the estimated quantities. Compared with SIMs using no priori-knowledge, the proposed method could reduce the estimation error variances of the system matrices B and D, as supported by variance analysis. A prior knowledge-based subspace identification method is proposed for batch processes subject to repeatable disturbances. Abstract: In this paper, a prior-knowledge-based subspace identification method (SIM) is proposed for batch processes subject to repeatable disturbances. The proposed method is a two-step procedure for state-space model identification: in the first step, the extended observability and triangular Toeplitz matrices are estimated simultaneously from a parity space of the experimental data and, based on which, the corresponding system matrices are retrieved in the second step. More specifically, A and C are retrieved from the estimated extended observability matrix, while B and D are retrieved from the estimated triangular Toeplitz matrix. The proposed method shows several superiorities in the following aspects. Firstly, it is able to provide unbiased parameter estimation in the presence of repeatable disturbances, thanks to the proposed difference operator which eliminates the disturbance effect. Secondly, it shows better robustness to measurement noise compared with the existing SIMs using parity space, due to the inherent instrumental variable mechanism and the new technique to build the instrument, which greatly enhance the estimated model efficiency/accuracy. Lastly, by taking the auxiliary static-gain information into account in the identification procedure, the variance properties of the parameters can be improved, especially for the system matrices B and D . All the above-mentioned developments are analyzed with strict mathematical proofs, along with two illustrative examples to confirm the effectiveness and merits.