Evaluation of experiment designs for MIMO system identification by model predictive control

Kurt Erik Häggblom

doi:10.1109/CCA.2015.7320628

Evaluation of experiment designs for MIMO system identification by model predictive control

Kurt Erik Häggblom

Research output: Chapter in Book/Conference proceeding › Conference contribution › Scientific › peer-review

3 Citations (Scopus)

24 Downloads (Pure)

Abstract

Ten different experiment designs for control-oriented MIMO identification are evaluated by the performance of model predictive control (MPC) using the identified models for control of the true system, which is a moderately ill-conditioned 3x3 system. The evaluated designs are some standard designs as well as advanced designs aimed at ensuring integral controllability requirements. Different types of input perturbations are also considered. Control performance in terms of output error and control activity measures is determined for various choices of sampling time and input constraints. The evaluation clearly shows that for this application, experiment designs where the directionality of the system is taken into account produce better models for control design than more standard methods.

Original language	Undefined/Unknown
Title of host publication	2015 IEEE Conference on Control Applications (CCA)
Publisher	IEEE
Pages	169–174
ISBN (Print)	978-1-4799-7787-1
DOIs	https://doi.org/10.1109/CCA.2015.7320628
Publication status	Published - 2015
MoE publication type	A4 Article in a conference publication
Event	IEEE Conference on Control Applications - 2015 IEEE Conference on Control Applications (CCA). Part of 2015 IEEE Multi-Conference on Systems and Control (MSC). Duration: 21 Sept 2015 → 23 Sept 2015

Conference

Conference	IEEE Conference on Control Applications
Period	21/09/15 → 23/09/15

Keywords

Model predictive control
Modeling
system identification

Access to Document

10.1109/CCA.2015.7320628

2015MSC_0413_FI.pdf

Cite this

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title = "Evaluation of experiment designs for MIMO system identification by model predictive control",

abstract = "Ten different experiment designs for control-oriented MIMO identification are evaluated by the performance of model predictive control (MPC) using the identified models for control of the true system, which is a moderately ill-conditioned 3x3 system. The evaluated designs are some standard designs as well as advanced designs aimed at ensuring integral controllability requirements. Different types of input perturbations are also considered. Control performance in terms of output error and control activity measures is determined for various choices of sampling time and input constraints. The evaluation clearly shows that for this application, experiment designs where the directionality of the system is taken into account produce better models for control design than more standard methods.",

keywords = "Model predictive control, Modeling, system identification, Model predictive control, Modeling, system identification, Model predictive control, Modeling, system identification",

author = "H{\"a}ggblom, {Kurt Erik}",

note = "rt. 2015 IEEE Conference on Control Applications (CCA). Part of 2015 IEEE Multi-Conference on Systems and Control, September 21-23, 2015. Sydney, Australia; IEEE Conference on Control Applications ; Conference date: 21-09-2015 Through 23-09-2015",

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N1 - rt. 2015 IEEE Conference on Control Applications (CCA). Part of 2015 IEEE Multi-Conference on Systems and Control, September 21-23, 2015. Sydney, Australia

PY - 2015

Y1 - 2015

N2 - Ten different experiment designs for control-oriented MIMO identification are evaluated by the performance of model predictive control (MPC) using the identified models for control of the true system, which is a moderately ill-conditioned 3x3 system. The evaluated designs are some standard designs as well as advanced designs aimed at ensuring integral controllability requirements. Different types of input perturbations are also considered. Control performance in terms of output error and control activity measures is determined for various choices of sampling time and input constraints. The evaluation clearly shows that for this application, experiment designs where the directionality of the system is taken into account produce better models for control design than more standard methods.

AB - Ten different experiment designs for control-oriented MIMO identification are evaluated by the performance of model predictive control (MPC) using the identified models for control of the true system, which is a moderately ill-conditioned 3x3 system. The evaluated designs are some standard designs as well as advanced designs aimed at ensuring integral controllability requirements. Different types of input perturbations are also considered. Control performance in terms of output error and control activity measures is determined for various choices of sampling time and input constraints. The evaluation clearly shows that for this application, experiment designs where the directionality of the system is taken into account produce better models for control design than more standard methods.

KW - Model predictive control

KW - Modeling

KW - system identification

KW - Model predictive control

KW - Modeling

KW - system identification

KW - Model predictive control

KW - Modeling

KW - system identification

U2 - 10.1109/CCA.2015.7320628

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BT - 2015 IEEE Conference on Control Applications (CCA)

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T2 - IEEE Conference on Control Applications

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