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Publication
Comparison of off-line and on-line performance of alternative neural network models
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Advisor(s)
Abstract(s)
The Proportional Integral and Derivative
(PID) controller is often used in industrial
applications due to its functional simplicity
and robust performance. Autotuning methods
for these simple controllers are economically
important. In order to accomplish this auto
tuning in real time, without perturbing the
closed-loop operation, models of criteria that are intended to be optimised are needed. In this paper, the ITAE criterion will be employed, as responses obtained with this criterion are well damped. In this paper neural networks are proposed as tools that allow these kinds of mappings. To improve the autotuner performance in a continuous operation, these models should be updated online.
This way, the corresponding neural networks, after being trained off-line should be adapted on-line in real time. In the present
work, the off-line and on-line performances of Multi-layer Perceptrons (MLPs), Radial Basis Function (RBFs) and Basis-Spline neural
networks (B-splines), are analysed and
compared.
Description
Keywords
On-line learning Neural Networks PID autotuning
Citation
Lima, J. M. G.; Ruano, A. E. Comparison of off-line and on-line performance of alternative neural network models, Trabalho apresentado em Information Processing and Management of Uncertainity in Knowledge Based Systems (IPMU 2000), In Information Processing and Management of Uncertainity in Knowledge Based Systems (IPMU 2000), Madrid, 2000.