Laboratory demonstration for model predictive multivariable control with a coupled drive system

Su, SW; Nguyen, HT; Ha, QP

Laboratory demonstration for model predictive multivariable control with a coupled drive system

Su, SW

Nguyen, HT

Ha, QP

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Publication Type:: Conference Proceeding
Citation:: 11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010, 2010, pp. 762 - 767
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	Su, SW https://orcid.org/0000-0002-5720-8852	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010, 2010, pp. 762 - 767	en_US
dc.identifier.isbn	9781424478132	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16480
dc.description.abstract	Teaching multivariable control usually involves a certain level of mathematical sophistication and hence requires some labaratorial exemplification of the material given in formal lectures. This paper reports on a hands-on approach to multivariable control education via the implementation of a model predictive controller on a two-input, two output coupled drive apparatus. This scaled-down system represents many industrial processes while provides an excellent set-up for demonstrating the cross-coupled effects in multi-input multi-output systems. Here, a model predictive controller (MPC) is developed and implemented on the basis of a constrained optimization problem to show control performance via the belt tension and velocity outputs, demonstrate the decoupling capability, and also illustrate such issues as control input saturation, the selection of operating point, reference inputs, and system robustness to external disturbance and varying parameters. The implementation is based on Labview and MATLAB Model Predictive Control Toolbox. ©2010 IEEE. Model predictive Control.	en_US
dc.relation.ispartof	11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010	en_US
dc.relation.isbasedon	10.1109/ICARCV.2010.5707919	en_US
dc.title	Laboratory demonstration for model predictive multivariable control with a coupled drive system	en_US
dc.type	Conference Proceeding
utslib.for	090602 Control Systems, Robotics and Automation	en_US
dc.location.activity	Singapore	en_US
dc.location.activity	Singapore, SINGAPORE
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

Teaching multivariable control usually involves a certain level of mathematical sophistication and hence requires some labaratorial exemplification of the material given in formal lectures. This paper reports on a hands-on approach to multivariable control education via the implementation of a model predictive controller on a two-input, two output coupled drive apparatus. This scaled-down system represents many industrial processes while provides an excellent set-up for demonstrating the cross-coupled effects in multi-input multi-output systems. Here, a model predictive controller (MPC) is developed and implemented on the basis of a constrained optimization problem to show control performance via the belt tension and velocity outputs, demonstrate the decoupling capability, and also illustrate such issues as control input saturation, the selection of operating point, reference inputs, and system robustness to external disturbance and varying parameters. The implementation is based on Labview and MATLAB Model Predictive Control Toolbox. ©2010 IEEE. Model predictive Control.

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http://hdl.handle.net/10453/16480