Neuro-Evolution of Continuous-Time Dynamic Process Controllers
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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.
KeywordsContinuous-Time Controller, Non-linear Dynamic System, Artificial Neural Network, Genetic Algorithm-Based Learning, Control Performance
Document typePeer reviewed
Document versionFinal PDF
SourceMendel. 2021 vol. 27, č. 2, s. 7-11. ISSN 1803-3814
- Vol. 27, No. 2