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Practical experience with unified discrete and continuous–time, multi–input identification for control system design

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Practical experience with unified discrete and continuous–time, multi–input identification for control system design. / Taylor, C. James; Young, Peter; Cross, Philip.

16th IFAC Symposium on System Identification. ed. / Michel Kinnaert. Brussels : IFAC, 2012.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

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Taylor, C. James ; Young, Peter ; Cross, Philip. / Practical experience with unified discrete and continuous–time, multi–input identification for control system design. 16th IFAC Symposium on System Identification. editor / Michel Kinnaert. Brussels : IFAC, 2012.

Bibtex

@inproceedings{f8241ae21f63494fa248e2f41e80d226,
title = "Practical experience with unified discrete and continuous–time, multi–input identification for control system design",
abstract = "The paper is concerned with the practical aspects of a unified approach to the identification and estimation of multiple-input, single-output (MISO) transfer function models for both continuous and discrete-time systems. The estimation algorithms considered in the paper are based on the Refined Instrumental Variable (RIV) approach to identification and estimation, where the MISO model denominator polynomials are normally constrained to be equal. Unconstrained RIV estimation presents a more difficult problem and it is necessary to exploit an iterative, back-fitting routine to handle this more general situation. The paper focuses on the practical realization of this back-fitting algorithm, including its initiation from either common denominator MISO or repeated SISO estimation. The rivcdd algorithm for continuous-time model estimation, as implemented in the CAPTAIN Toolbox for Matlab is then used in three practical examples: first, the modelling of solute transport and dispersion in a water body; secondly, modelling for two control problems, namely a pair of connected laboratory DC motors and a nonlinear wind turbine simulation.",
keywords = "identification, discrete-time, continuous-time, instrumental variables, MISO",
author = "Taylor, {C. James} and Peter Young and Philip Cross",
year = "2012",
doi = "10.3182/20120711-3-BE-2027.00123",
language = "English",
editor = "Michel Kinnaert",
booktitle = "16th IFAC Symposium on System Identification",
publisher = "IFAC",

}

RIS

TY - GEN

T1 - Practical experience with unified discrete and continuous–time, multi–input identification for control system design

AU - Taylor, C. James

AU - Young, Peter

AU - Cross, Philip

PY - 2012

Y1 - 2012

N2 - The paper is concerned with the practical aspects of a unified approach to the identification and estimation of multiple-input, single-output (MISO) transfer function models for both continuous and discrete-time systems. The estimation algorithms considered in the paper are based on the Refined Instrumental Variable (RIV) approach to identification and estimation, where the MISO model denominator polynomials are normally constrained to be equal. Unconstrained RIV estimation presents a more difficult problem and it is necessary to exploit an iterative, back-fitting routine to handle this more general situation. The paper focuses on the practical realization of this back-fitting algorithm, including its initiation from either common denominator MISO or repeated SISO estimation. The rivcdd algorithm for continuous-time model estimation, as implemented in the CAPTAIN Toolbox for Matlab is then used in three practical examples: first, the modelling of solute transport and dispersion in a water body; secondly, modelling for two control problems, namely a pair of connected laboratory DC motors and a nonlinear wind turbine simulation.

AB - The paper is concerned with the practical aspects of a unified approach to the identification and estimation of multiple-input, single-output (MISO) transfer function models for both continuous and discrete-time systems. The estimation algorithms considered in the paper are based on the Refined Instrumental Variable (RIV) approach to identification and estimation, where the MISO model denominator polynomials are normally constrained to be equal. Unconstrained RIV estimation presents a more difficult problem and it is necessary to exploit an iterative, back-fitting routine to handle this more general situation. The paper focuses on the practical realization of this back-fitting algorithm, including its initiation from either common denominator MISO or repeated SISO estimation. The rivcdd algorithm for continuous-time model estimation, as implemented in the CAPTAIN Toolbox for Matlab is then used in three practical examples: first, the modelling of solute transport and dispersion in a water body; secondly, modelling for two control problems, namely a pair of connected laboratory DC motors and a nonlinear wind turbine simulation.

KW - identification

KW - discrete-time

KW - continuous-time

KW - instrumental variables

KW - MISO

U2 - 10.3182/20120711-3-BE-2027.00123

DO - 10.3182/20120711-3-BE-2027.00123

M3 - Conference contribution/Paper

BT - 16th IFAC Symposium on System Identification

A2 - Kinnaert, Michel

PB - IFAC

CY - Brussels

ER -