Rank reduction and volume minimization approach to state-space subspace system identification

2006 ◽  
Vol 86 (11) ◽  
pp. 3275-3285 ◽  
Author(s):  
Berkant Savas ◽  
David Lindgren
Keyword(s):  
System Identification ◽  
State Space ◽  
Rank Reduction ◽  
Volume Minimization ◽  
Subspace System Identification ◽  
Minimization Approach

scholarly journals Model Predictive Control Method of Simulated Moving Bed Chromatographic Separation Process Based on Subspace System Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-24 ◽  
Author(s):  
Zhen Yan ◽  
Jie-Sheng Wang ◽  
Shao-Yan Wang ◽  
Shou-Jiang Li ◽  
Dan Wang ◽  
...  
Keyword(s):  
System Identification ◽  
State Space ◽  
Predictive Control ◽  
Chromatographic Separation ◽  
Control Method ◽  
Separation Process ◽  
Simulated Moving Bed ◽  
Moving Bed ◽  
Separation Technology ◽  
Subspace System Identification

Simulated moving bed (SMB) chromatographic separation is a new type of separation technology based on traditional fixed bed adsorption operation and true moving bed (TMB) chromatographic separation technology, which includes inlet-outlet liquid, liquid circulation, and feed liquid separation. The input-output data matrices were constructed based on SMB chromatographic separation process data. The SMB chromatographic separation process was modeled by utilizing two subspace system identification algorithms: multivariable output-error state-space (MOESP) identification algorithm and numerical algorithms for subspace state-space system identification (N4SID), so as to obtain the 3rd-order and 4th-order state-space yield models of the SMB chromatographic separation process, respectively. The model predictive control method based on the established state-space models is used in the SMB chromatographic separation process. The influence of different control indicators on the predictive control system response performance is discussed. The output response curves of the yield models were obtained by changing the related parameters so that the yield model parameters are optimized set meanwhile. Finally, the simulation results showed that the yield models are successfully controlled based on the each control period and given yield range.

scholarly journals A Comparative Study of the Data-Driven Stochastic Subspace Methods for Health Monitoring of Structures: A Bridge Case Study

2020 ◽  
Vol 10 (9) ◽  
pp. 3132 ◽  
Author(s):  
Hoofar Shokravi ◽  
Hooman Shokravi ◽  
Norhisham Bakhary ◽  
Seyed Saeid Rahimian Koloor ◽  
Michal Petrů
Keyword(s):  
System Identification ◽  
State Space ◽  
Health Monitoring ◽  
Modal Identification ◽  
Canonical Variate ◽  
Lumped Mass ◽  
Mass System ◽  
Principle Component ◽  
Computation Efficiency ◽  
Subspace System Identification

Subspace system identification is a class of methods to estimate state-space model based on low rank characteristic of a system. State-space-based subspace system identification is the dominant subspace method for system identification in health monitoring of the civil structures. The weight matrices of canonical variate analysis (CVA), principle component (PC), and unweighted principle component (UPC), are used in stochastic subspace identification (SSI) to reduce the complexity and optimize the prediction in identification process. However, researches on evaluation and comparison of weight matrices’ performance are very limited. This study provides a detailed analysis on the effect of different weight matrices on robustness, accuracy, and computation efficiency. Two case studies including a lumped mass system and the response dataset of the Alamosa Canyon Bridge are used in this study. The results demonstrated that UPC algorithm had better performance compared to two other algorithms. It can be concluded that though dimensionality reduction in PC and CVA lingered the computation time, it has yielded an improved modal identification in PC.

scholarly journals Application of the Subspace-Based Methods in Health Monitoring of Civil Structures: A Systematic Review and Meta-Analysis

2020 ◽  
Vol 10 (10) ◽  
pp. 3607
Author(s):  
Hoofar Shokravi ◽  
Hooman Shokravi ◽  
Norhisham Bakhary ◽  
Mahshid Heidarrezaei ◽  
Seyed Saeid Rahimian Koloor ◽  
...  
Keyword(s):  
System Identification ◽  
Systematic Reviews ◽  
Health Monitoring ◽  
Meta Analysis ◽  
Civil Structures ◽  
Literature Reviews ◽  
Research Gap ◽  
Meta Analyses ◽  
Subspace System Identification ◽  
Insight Into

A large number of research studies in structural health monitoring (SHM) have presented, extended, and used subspace system identification. However, there is a lack of research on systematic literature reviews and surveys of studies in this field. Therefore, the current study is undertaken to systematically review the literature published on the development and application of subspace system identification methods. In this regard, major databases in SHM, including Scopus, Google Scholar, and Web of Science, have been selected and preferred reporting items for systematic reviews and meta-analyses (PRISMA) has been applied to ensure complete and transparent reporting of systematic reviews. Along this line, the presented review addresses the available studies that employed subspace-based techniques in the vibration-based damage detection (VDD) of civil structures. The selected papers in this review were categorized into authors, publication year, name of journal, applied techniques, research objectives, research gap, proposed solutions and models, and findings. This study can assist practitioners and academicians for better condition assessment of structures and to gain insight into the literature.

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