Neuro-Evolution of Continuous-Time Dynamic Process Controllers

  • Ivan Sekaj Slovak University of Technology in Bratislava
  • Ivan Kénický Slovak University of Technology in Bratislava
  • Filip Zúbek Slovak University of Technology in Bratislava
Keywords: Continuous-Time Controller, Non-linear Dynamic System, Artificial Neural Network, Genetic Algorithm-Based Learning, Control Performance

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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Published
2021-12-21
How to Cite
[1]
Sekaj, I., Kénický, I. and Zúbek, F. 2021. Neuro-Evolution of Continuous-Time Dynamic Process Controllers. MENDEL. 27, 2 (Dec. 2021), 7-11. DOI:https://doi.org/10.13164/mendel.2021.2.007.
Section
Articles