Artificial neural network and fuzzy logic controller for GTAW modeling and control

The purpose of this study is to develop a model of neuro-fuzzy regulation of the microclimate in the room. The proposed model consists of an artificial neural network serving to form a comfort index PMV, a fuzzy logic controller for regulating temperature and humidity in the room. This approach makes it easy to manage these parameters through an estimate of the PMV index, which indicates the level of thermal comfort in the room.

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Ultracapacitor modeling and control with discrete fractional order artificial neural network

Proceedings of the 13th International Carpathian Control Conference (ICCC) ◽

10.1109/carpathiancc.2012.6228720 ◽

2012 ◽

Cited By ~ 1

Author(s):

Grzegorz Sarwas ◽

Dominik Sierociuk ◽

Andrzej Dzielinski

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fractional Order ◽

Modeling And Control ◽

Artificial Neural ◽

And Control

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Artificial neural network modeling and control of paper machine headbox parameters

Smart Computing ◽

10.1201/9781003167488-92 ◽

2021 ◽

pp. 733-742

Author(s):

Rajesh Kumar ◽

A.K. Ray

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Modeling ◽

Neural Network Modeling ◽

Paper Machine ◽

Modeling And Control ◽

Artificial Neural Network Modeling ◽

Artificial Neural ◽

And Control ◽

Paper Machine Headbox

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Modeling and control of transverse coupled bunch mode levels in Indus-2 using artificial neural network

Review of Scientific Instruments ◽

10.1063/5.0016339 ◽

2021 ◽

Vol 92 (3) ◽

pp. 033304

Author(s):

Surendra Yadav ◽

T. A. Puntambekar ◽

P. V. Varde

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Modeling And Control ◽

Artificial Neural ◽

And Control

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A comparative study of fuzzy logic controller and artificial neural network in speed control of separately excited DC motor

2017 7th IEEE International Conference on Control System, Computing and Engineering (ICCSCE) ◽

10.1109/iccsce.2017.8284430 ◽

2017 ◽

Cited By ~ 4

Author(s):

M. A. H. Azman ◽

J. M. Aris ◽

Z. Hussain ◽

A. A. A. Samat ◽

A. M. Nazelan

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fuzzy Logic ◽

Comparative Study ◽

Fuzzy Logic Controller ◽

Speed Control ◽

Dc Motor ◽

Artificial Neural

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Comparison of BLDC Motor Controller Design for Electric Vehicles Using Fuzzy Logic Controller and Artificial Neural Network

PRZEGLĄD ELEKTROTECHNICZNY ◽

10.15199/48.2021.06.01 ◽

2021 ◽

Vol 1 (6) ◽

pp. 3-11

Author(s):

Feby Pamuji

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fuzzy Logic ◽

Electric Vehicles ◽

Fuzzy Logic Controller ◽

Controller Design ◽

Bldc Motor ◽

Motor Controller ◽

Artificial Neural

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PERFORMA PENGENDALI ARUS STATOR DENGAN MENGGUNAKAN ANFIS PADA PENGEMUDIAN MOTOR INDUKSI BERBASIS VECTOR CONTROL

INOVTEK POLBENG ◽

10.35314/ip.v9i1.987 ◽

2019 ◽

Vol 9 (1) ◽

pp. 169

Author(s):

Muhammad Rizani Rusli

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fuzzy Logic ◽

Vector Control ◽

Fuzzy Logic Controller ◽

Matlab Simulink ◽

Artificial Neural

Vector control terdiri atas dua pengendali arus stator dq-axis. Performa pengendalian motor induksi secara keseluruhan bergantung pada salah satu atau kedua pengendali arus stator tersebut. Umumnya pengendali arus stator menggunakan pengendali PI, namun pengendali ini memiliki beberapa kelemahan utama yaitu susahnya menentukan gain dari proportional maupun integral. ANFIS yang menggabungkan fuzzy logic controller dan artificial neural network menawarkan kemampuan training, adaptif, cepat, dan handal. Pada paper ini pengendali ANFIS diterapkan untuk pengendali arus stator d-axis pada pengemudian motor induksi berdaya 10 HP dengan metode pengemudian vector control. Keseluruhan sistemnya divalidasi melalui MATLAB/Simulink. Pengendali ANFIS dibandingkan dengan pengendali PI untuk mengevaluasi performa dari motor. Evaluasi performa yang diamati yaitu performa kecepatan dinamik dan performa arus dengan skema pengujian berbeban konstan dan bervariasi. Dari kedua pengujian, pengendali arus stator d-axis PI dan ANFIS menghasilkan trend respon kecepatan dinamik yang sama, namun pengendali arus stator d-axis ANFIS mampu mereduksi konsumsi arus fasa, ripple arus stator d-axis, dan THD arus fasa.

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