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Prediction interval-based control of nonlinear systems using neural networks

conference contribution
posted on 2015-01-01, 00:00 authored by Anwar HosenAnwar Hosen, Abbas KhosraviAbbas Khosravi, Saeid Nahavandi, Douglas CreightonDouglas Creighton
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.

History

Event

Neural Information Processing. Conference (22nd : 2015 : Istanbul, Turkey)

Series

Lecture Notes in Computer Science; 9492

Pagination

101 - 110

Publisher

Springer

Location

Istanbul, Turkey

Place of publication

Berlin, Germany

Start date

2015-11-09

End date

2015-11-12

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319265544

Language

eng

Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2015, Springer

Title of proceedings

ICONIP 2015 : Neural information processing : 22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015 : proceedings