Prediction interval-based control of nonlinear systems using neural networks

Hosen, Mohammad Anwar, Khosravi, Abbas, Nahavandi, Saeid and Creighton, Douglas 2015, Prediction interval-based control of nonlinear systems using neural networks, in ICONIP 2015 : Neural information processing : 22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015 : proceedings, Springer, Berlin, Germany, pp. 101-110, doi: 10.1007/978-3-319-26555-1_12.

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Title Prediction interval-based control of nonlinear systems using neural networks
Author(s) Hosen, Mohammad AnwarORCID iD for Hosen, Mohammad Anwar orcid.org/0000-0001-8282-3198
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Creighton, DouglasORCID iD for Creighton, Douglas orcid.org/0000-0002-9217-1231
Conference name Neural Information Processing. Conference (22nd : 2015 : Istanbul, Turkey)
Conference location Istanbul, Turkey
Conference dates 9-12 Nov. 2015
Title of proceedings ICONIP 2015 : Neural information processing : 22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015 : proceedings
Editor(s) Arik, Sabri
Huang, Tingwen
Lin, Weng Kin
Liu, Qingshan
Publication date 2015
Series Lecture Notes in Computer Science; 9492
Start page 101
End page 110
Total pages 10
Publisher Springer
Place of publication Berlin, Germany
Keyword(s) Prediction interval
PI-based model
PI-based controller
Neural network
Polymerization reactor
Summary Prediction interval (PI) is a promising tool for quantifying uncertainties associated with point predictions. Despite its informativeness, the design and deployment of PI-based controller for complex systems is very rare. As a pioneering work, this paper proposes a framework for design and implementation of PI-based controller (PIC) for nonlinear systems. Neural network (NN)-based inverse model within internal model control structure is used to develop the PIC. Firstly, a PI-based model is developed to construct PIs for the system output. This model is then used as an online estimator for PIs. The PIs from this model are fed to the NN inverse model along with other traditional inputs to generate the control signal. The performance of the proposed PIC is examined for two case studies. This includes a nonlinear batch polymerization reactor and a numerical nonlinear plant. Simulation results demonstrated that the proposed PIC tracking performance is better than the traditional NN-based controller.
ISBN 9783319265544
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-26555-1_12
Field of Research 090407 Process Control and Simulation
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082486

Document type: Conference Paper
Collections: Centre for Intelligent Systems Research
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