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
Pagination
101-110
Location
Istanbul, Turkey
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
Event
Neural Information Processing. Conference (22nd : 2015 : Istanbul, Turkey)