Simple and Robust Experiment Design for System Identification Using Fractional Models

This paper tackles the problems of simple and robust experiment design for system identification using elementary fractional models. It is based on a frequency domain approach and allows to determine the best sine input signal maximizing D-optimality criterion of the parameters inverse covariance ma...

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Bibliographic Details
Published inIEEE transactions on automatic control Vol. 62; no. 6; pp. 2648 - 2658
Main Authors Abrashov, Sergey, Malti, Rachid, Moze, Mathieu, Moreau, Xavier, Aioun, Francois, Guillemard, Franck
Format Journal Article
LanguageEnglish
Published New York IEEE 01.06.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Subjects
Automatic
Covariance matrices
Covariance matrix
Criteria
Design engineering
Design of experiments
Design parameters
Engineering Sciences
Estimation
Experiment design
Experiments
fractional models
Mathematical models
Optimization
Optimized production technology
Parameter estimation
Robustness
System identification
Transfer functions
Uncertainty
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Summary:This paper tackles the problems of simple and robust experiment design for system identification using elementary fractional models. It is based on a frequency domain approach and allows to determine the best sine input signal maximizing D-optimality criterion of the parameters inverse covariance matrix in different contexts? First, a single parameter (any of the parameters of the elementary fractional model) is assumed to be unknown. Next, any combination of two and then three parameters are supposed to be unknown. Finally, the problem of robust experiment design is treated when a bounded interval of the estimated parameters is known, in the same contexts.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2016.2614910