Application of adaptive controller neural network based on RBF NN for temperature control electrical resistance furnace

• Autorzy: Kada Benyekhlef , ElKebir Abdelkader , Negadi Karim , Belhadj Hafida , Chaouch Djamel Eddine

Identyfikatory

DOI

10.15199/48.2020.06.06

Warianty tytułu

PL

Zastosowanie sterowania adaptacyjnego bazującego na sieci neuronowej do kontroli temperatury pieca elektrycznego

• Języki publikacji: EN

Abstrakty

EN

In order to solve the problem for temperature electrical resistance furnace. Characterized by their large inertia, nonlinear, long time delay and time-varying property it is rather difficult to obtain satisfactory control results with Performances of conventional PI control cannot achieve good control effect. In this paper a neural network-based adaptive control approach (ACNN) for electrical furnace is developed .using RBF NN to estimate the unknown functions by neural networks and from good choice of the law of adaptation. Based on the resolution of the lyapunov equation. Taking account of all possible parameter variations the adaptive control is designed so that it has the ability to improve the performance of the closed loop system, producing the control signal by using the information from the system. In this case we use a coping mechanism that observes the signal to control and adjust the synaptic weights of neural networks when system parameters change over time. Result shows that the proposed algorithm (ACNN) performs very well when furnace parameter varies the latter allow the neural model to be identified online and, if necessary its parameters to be stabilized and it is very easy to program it online.

PL

Piec elektryczny charakteryzuje się nieliniowością, dużym czasem opóźnienia co utrudnia sterowanie nime. W pracy zaproponowano system sterowania piecem z wykorzystaniem sieci neuronowej System jest zaprojektowany tak, że uwzględnia zmiany parametrów.

Słowa kluczowe

EN

electric resistance furnace; integral control with compensation poles and zeros; online learning; inverse neural model control; model reference adaptive control; neural network

PL

piec elektryczny; sieć neuronowa; sterowanie temperaturą; model odniesienia; sterowanie adaptacyjne

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2020
• Tom: R. 96, nr 6
• Strony: 33--38
• Opis fizyczny: Bibliogr. 15 poz., rys., wykr.

Twórcy

autor

Kada Benyekhlef

• Department of Electrical Engineering, University Mustafa Stambouli of Mascara, Algeria

autor

ElKebir Abdelkader

• Department of Electrical Engineering, University Mustafa Stambouli of Mascara, Algeria

autor

Negadi Karim

• University Ibn Khaldoun of Tiaret, BP 78 Size Zarroura, Tiaret 14000, Algeria

autor

Belhadj Hafida

• Department of physic University Mustafa Stambouli of Mascara, Algeria

autor

Chaouch Djamel Eddine

• Department of Physic University Mustafa Stambouli of Mascara, Algeria

Bibliografia

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• [2] A. ELKebir, H. Belhadj, A Chaker, K. Negadi “.Internal Model Control Based on GANN for A Temperature Control Electrical Furnace” International Review on Modeling and Simulations (I.R.EMOS.) Vol 7, No 5 (2014) PP.884-892.
• [3] O. Dubois, J. Nicolas, A.Billat, Adaptive Neural Network Control of the Temperature in an Oven. Advance in Neural Networks for Control and Systems, IEEE publication date 25-27 may 1994, pp. 81 – 83
• [4] B1.Srilakshmi B1, K2.Venkataratnam , “Temperature Control of Electric Furnace using Genetic Algorithm based PID controller” International Journal of Advanced Engineering and Global Technology Vol-03, Issue-11, December 2015.
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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).