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Wyświetlanie 1-2 z 2
Tytuł:
Based on neural network adaptive linear quadratic regulator for inverter with voltage matching circuit
Autorzy:
Niewiara, Ł.
Tarczewski, T.
Grzesiak, L.M.
Powiązania:
https://bibliotekanauki.pl/articles/97686.pdf
Data publikacji:
2014
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
adaptive linear-quadratic regulator
voltage matching circuit
artificial neural network
current ripple
Opis:
This paper describes a discrete adaptive linear quadratic regulator used to load current control in terms of variable DC voltage of inverter. Controller was designed by using linear quadratic optimization method. Adaptive LQR was used because of non-stationarity of the control system caused by Voltage Matching Circuit - VMC. Gain values of the adaptive controller were approximated by using an artificial neural network. The VMC was realized as an additional buck converter integrated with the main inverter. As the load of the 2-level inverter a 3-phase symmetric RL circuit was used. Simulation tests show the behavior of the load current regulation during DC bus voltage level step changes. The dependence between current RMS value and inverter DC bus voltage level was also shown. There were also made a comparison of the traditional 2-level inverter structure with the modified structure uses VMC. Simulation test was made by using Matlab Simulink and PLECS software.
Źródło:
Computer Applications in Electrical Engineering; 2014, 12; 434-443
1508-4248
Pojawia się w:
Computer Applications in Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
“P” coordinator scheme and interaction prediction principle in hierarchical structure of ANN
Autorzy:
Płaczek, S.
Powiązania:
https://bibliotekanauki.pl/articles/97277.pdf
Data publikacji:
2015
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
Artificial Neural Network (ANN)
hierarchy
decomposition
coordination
coordination principle
P-regulator
feedback principle
Opis:
When implementing the hierarchical structure [4][5] of the learning algorithm of an Artificial Neural Network (ANN), two very important questions have to be solved. The first one is connected with the selection of the broad coordination principle. In [1], three different principles are described. They vary with regard to the degree of freedom for the first-level tasks. The second problem is connected with the coordinator structure or, in other words, the coordination algorithm. In the regulation theory, the process of finding the coordinator structure is known as the feedback principle. The simplest regulator structure (scheme) is known as the proportional regulator – “P” regulator. In the article, the regulator structure and its parameters are analysed as well as their impact on the learning process quality.
Źródło:
Computer Applications in Electrical Engineering; 2015, 13; 319-329
1508-4248
Pojawia się w:
Computer Applications in Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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