Case studies on closed-loop identification for MPC

Control Engineering Practice

Volumn 10, Issue 4, 2002, Pages 403-417

  Zhu, Yucai ^a,b   Butoyi, Firmin ^c  

^a EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

^b Tai Ji Control   (Netherlands)

^c DOW BENELUX B V   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; DISTILLATION; ETHYLENE; MULTIVARIABLE CONTROL SYSTEMS;

MODEL PREDICTIVE CONTROL (MPC);

CLOSED LOOP CONTROL SYSTEMS;

DISTILLATION; DYNAMIC MODELING; ETHYLENE; PREDICTIVE CONTROL; PROCESS CONTROL;

EID: 0036532180     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0967-0661(02)00007-2     Document Type: Article

References (24)

1. Application of a large predictive multivariable controller to a hydrocracher second stage reactor
2. Optimal experiment design with respect to the intended model application
3. Quantifying the error in estimated transfer functions with application to model order selection
4. A stochastic embedding approach for quantifying uncertainty in the estimation of restricted complexity models
5. A generalized orthonormal basis for basis for linear dynamical system
6. For model-based control design, closed-loop identification gives better performance
7. Design of identification experiments for robust control. A geometric approach for bivariate processes
8. Canonical variable analysis in identification, filtering and adaptive control
9. Asymptotic variance expressions for identified blackbox transfer function models
10. Dynamics and control of distillation columns- - A tutorial introduction
11. Increase MPC project efficiency by using a modern identification method
12. Some study on identification of ill-conditioned processes for control
13. Generalized binary noise test-signal concept for improved identification-experiment design
14. Closed-loop issues in system identification
15. N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems
16. Identification of the deterministic part of MIMO state space models given in innovations form from input-output data
17. System identification using Laguerre models
18. Black-box identification of MIMO transfer functions: Asymptotic properties of prediction error models
19. Multivariable process identification for MPC: The asymptotic method and its applications
20. Use of error criteria in identification for control