Neuro-Evolution of Continuous-Time Dynamic Process Controllers
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Date
2021-12-21
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Mark
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Institute of Automation and Computer Science, Brno University of Technology
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Abstract
Artificial neural networks are means which are, among several other approaches, effectively usable for modelling and control of non-linear dynamic systems. In case of modelling systems input and output signals are a-priori known, supervised learning methods can be used. But in case of controller design of dynamic systems the required (optimal) controller output is a-priori unknown, supervised learning cannot be used. In such case we only can define some criterion function, which represents the required control performance of the closed-loop system. We present a neuro-evolution design for control of a continuous-time controller of non-linear dynamic systems. The controller is represented by an MLP-type artificial neural network. The learning algorithm of the neural network is based on an evolutionary approach with genetic algorithm. An integral-type performance index representing control quality, which is based on closed-loop simulation, is minimised. The results are demonstrated on selected experiments with controller reference value changes as well as with noisy system outputs.
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Mendel. 2021 vol. 27, č. 2, s. 7-11. ISSN 1803-3814
https://mendel-journal.org/index.php/mendel/article/view/153
https://mendel-journal.org/index.php/mendel/article/view/153
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Peer-reviewed
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en
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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license
http://creativecommons.org/licenses/by-nc-sa/4.0
http://creativecommons.org/licenses/by-nc-sa/4.0