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Design of an Adaptive Support Vector Regressor Controller for a Spherical Tank System Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Prediction Interval-Based Control of Nonlinear Systems Using Neural Networks

Authors : Mohammad Anwar Hosen, Abbas Khosravi, Saeid Nahavandi, Douglas Creighton

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

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.

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previous chapter Neuron-Synapse Level Problem Decomposition Method for Cooperative Neuro-Evolution of Feedforward Networks for Time Series Prediction
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Metadata
Title
Prediction Interval-Based Control of Nonlinear Systems Using Neural Networks
Authors
Mohammad Anwar Hosen
Abbas Khosravi
Saeid Nahavandi
Douglas Creighton
Copyright Year
2015
Book
Neural Information Processing

Print ISBN: 978-3-319-26554-4

Electronic ISBN: 978-3-319-26555-1

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-26555-1_12

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