Identification of the Tennessee Eastman challenge process with subspace methods

Control Engineering Practice

Volumn 9, Issue 12, 2001, Pages 1337-1351

  Juricek, Ben C ^a   Seborg, Dale E ^b   Larimore, Wallace E ^c  

^a TOYON RESEARCH CORPORATION   (United States)

^b University of California   (United States)

^c Adaptics Inc   (United States)

Author keywords

Case study; Subspace methods; System identification; Tennessee Eastman challenge process

Indexed keywords

ALGORITHMS; IDENTIFICATION (CONTROL SYSTEMS); PROCESS CONTROL; REGRESSION ANALYSIS; STATE SPACE METHODS;

PREDICTION ERROR METHODS;

CONTROL SYSTEM ANALYSIS;

CONTROL SYSTEM; PROCESS CONTROL;

EID: 0035674847     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0967-0661(01)00124-1     Document Type: Article

References (26)

1. Realization of stable models with subspace methods
2. A plant-wide industrial process control problem
3. A systematic comparison of control structures proposed to the Tennessee Eastman benchmark problem
4. Reduced-rank ARX and subspace models for process control
5. System identification, reduced-order filtering and modelling via canonical variate analysis
6. Statistical optimality and canonical variate analysis system identification
7. Optimal reduced rank modelling, prediction, monitoring, and control using canonical variate analysis
8. Automated multivariable system identification and industrial applications, Tutorial Paper
9. Guaranteed stability with subspace methods
10. Base control for the Tennessee Eastman problem
11. Optimal steady-state operation of the Tennessee Eastman challenge process
12. Decentralized control of the Tennessee Eastman challenge process
13. Nonlinear model predictive control of the Tennessee Eastman challenge process
14. Nonlinear modeling and state estimation for the Tennessee Eastman challenge process
15. An integrated identification and control design methodology for multivariable process system applications
16. Control of the Tennessee Eastman process using input-output models
17. Reduced-rank regression and canonical analysis
18. N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems