Optimally-Tuned Cascaded PID Control using Radial Basis Function Neural Network Metamodeling

Wahid, HB; Ha, QP; Mohamed Ali, MS

Optimally-Tuned Cascaded PID Control using Radial Basis Function Neural Network Metamodeling

Wahid, HB Ha, QP

Mohamed Ali, MS

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Publisher:: The University of Tasmania
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 3rd International Workshop on Artificial Intelligence in Science and Technology, 2009, pp. 1 - 6
Issue Date:: 2009-01

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Field	Value	Language
dc.contributor.author	Wahid, HB	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.contributor.author	Mohamed Ali, MS	en_US
dc.contributor.editor	Negnevistky, M	en_US
dc.date	2009-11-23	en_US
dc.date.issued	2009-01	en_US
dc.identifier.citation	Proceedings of the 3rd International Workshop on Artificial Intelligence in Science and Technology, 2009, pp. 1 - 6	en_US
dc.identifier.isbn	978-1-86295-550-9	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11083
dc.description.abstract	Dynamic systems are quite often non-linear and require a complex mathematical model. For their optimal control, it has been always a requirement to tune the controller parameters to achieve the best performance. Parameter tuning in complex systems is predominantly a time-consuming task, even with high performance computers. This paper provides an overview of metamodeling and demonstrates how it can be applied to efficiently tune the control parameters of a typically nonlinear and unstable process, the ball and beam system. Here, the metamodel is realized with a radial basis function (RBF) neural network to derive the PID parameters subject to an optimal criterion. The proposed approach is benchmarked with a commonly-used tuning technique.	en_US
dc.publisher	The University of Tasmania	en_US
dc.relation.ispartof	Proceedings of the 3rd International Workshop on Artificial Intelligence in Science and Technology	en_US
dc.relation.ispartof	International Workshop on Artificial Intelligence in Science and Technology	en_US
dc.title	Optimally-Tuned Cascaded PID Control using Radial Basis Function Neural Network Metamodeling	en_US
dc.type	Conference Proceeding
utslib.location	Hobart Tasmania Australia	en_US
utslib.location.activity	Hobart Tasmania	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	Hobart Tasmania	en_US
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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	open_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Hobart Tasmania Australia	en_US
pubs.start-date	2009-11-23	en_US

Abstract:

Dynamic systems are quite often non-linear and require a complex mathematical model. For their optimal control, it has been always a requirement to tune the controller parameters to achieve the best performance. Parameter tuning in complex systems is predominantly a time-consuming task, even with high performance computers. This paper provides an overview of metamodeling and demonstrates how it can be applied to efficiently tune the control parameters of a typically nonlinear and unstable process, the ball and beam system. Here, the metamodel is realized with a radial basis function (RBF) neural network to derive the PID parameters subject to an optimal criterion. The proposed approach is benchmarked with a commonly-used tuning technique.

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http://hdl.handle.net/10453/11083