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Process Systems Engineering
Constrained receding-horizon experiment design and parameter estimation in the presence of poor initial conditions
Article first published online: 29 DEC 2010
DOI: 10.1002/aic.12479
Copyright © 2010 American Institute of Chemical Engineers (AIChE)
Additional Information
How to Cite
Zhu, Y. and Huang, B. (2011), Constrained receding-horizon experiment design and parameter estimation in the presence of poor initial conditions. AIChE J., 57: 2808–2820. doi: 10.1002/aic.12479
Publication History
- Issue published online: 9 SEP 2011
- Article first published online: 29 DEC 2010
- Accepted manuscript online: 9 NOV 2010 12:13PM EST
- Manuscript Revised: 21 OCT 2010
- Manuscript Received: 27 APR 2010
Funded by
- Natural Sciences and Engineering Research Council of Canada
- Abstract
- Article
- References
- Cited By
Keywords:
- receding-horizon design;
- optimal experiment design;
- constrained EKF
Abstract
An optimal experiment design assumes the existence of an initial or nominal process model. The efficiency of this procedure depends on how the initial model is chosen. This creates a practical dilemma as estimating the model is precisely what the experiment tries to achieve. A novel approach to experiment design for identification of nonlinear systems is developed, with the purpose of reducing the influence of poor initial values. The experiment design and the parameter estimation are conducted iteratively under a receding-horizon framework. By taking steady-state prior knowledge into account, constraints on the parameters can be derived. Such constraints help reduce influence of poor initial models. The proposed algorithm is illustrated through examples to demonstrate its efficiency. © 2010 American Institute of Chemical Engineers AIChE J, 2011