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

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

scholarly journals The conceptual model of neuro-fuzzy regulation of the microclimate in the room

2018 ◽  
Vol 251 ◽  
pp. 03020
Author(s):  
Andrey Karpenko ◽  
Irina Petrova
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Thermal Comfort ◽  
Conceptual Model ◽  
Fuzzy Logic Controller ◽  
Comfort Index ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Artificial Neural

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.

scholarly journals Peningkatan Efisiensi Energi pada Kendaraan Listrik dengan Elektronik Diferensial Berbasis ANN (Artificial Neural Network)

2020 ◽  
Vol 8 (3) ◽  
pp. 642
Author(s):  
SOFYAN AHMADI ◽  
KHAIRUL ANAM ◽  
WIDJONARKO WIDJONARKO
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electric Vehicles ◽  
Average Power ◽  
Differential Method ◽  
Network Control ◽  
Bldc Motor ◽  
Vehicle Technology ◽  
Artificial Neural ◽  
Efficiency Test

ABSTRAKSeiring dengan perkembangan teknologi kendaraan listrik yang saat ini semakin canggih dan berkembang sangat cepat, upaya pengembangan kendaraan listrik terus dilakukan, salah satunya penggunaan motor BLDC dalam kendaraan listrik untuk meningkatkan efisiensi. Penelitian ini menggunakan kontrol ANN (Artificial Neural Network) pada mikrokontroler serta metode differential untuk pengontrolan kecepatan putar motor BLDC. Pengujian Percepatan dengan menempuh jarak 200 meter arus rata-rata sebesar 1,05 ampere. Daya rata-rata pada pengujian efisiensi sebesar 101 watt. Hasil efisiensi dari pengujian dengan panjang lintasan sejauh 3,3 km dengan waktu tempuh 10 menit didapatkan hasil efisiensi energi dari sistem kendaraan sebesar 179,34 km/kwh.Kata kunci: Motor BLDC, Elektronik Diferensial, Neural network-Logic, Akselerasi, Efisiensi. ABSTRACTAlong with the development of electric vehicle technology that is currently increasingly sophisticated and growing very fast. efforts to develop electric vehicles continue to be done, one of them the use of BLDC motor in electric vehicles to improve efficiency. In this study using ANN (Artificial Neural Network) control on the microcontroller as well as the differential method for controlling the rotational speed of the BLDC motor. Acceleration Testing with a distance of 200 meters average flow of 1.05 amperes. The average power on the 101 watt efficiency test. The efficiency of the test with the length of the track as far as 3.3 km with the travel time of 10 minutes obtained the efficiency of energy in the vehicle system of 179.34 km / kwh.Keywords: BLDC Motor, Electronic Differential, Neural network-Logic, Acceleration,Efficiency.

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

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.

scholarly journals Applicability of Fuzzy Logic and Artificial Neural Network for Unpaved Airfield Surface Bearing Strength Prediction

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3373
Author(s):  
Ludek Cicmanec
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Regression Function ◽  
Large Data ◽  
Soil Strength ◽  
Data Sets ◽  
Bearing Strength ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The main objective of this paper is to describe a building process of a model predicting the soil strength at unpaved airport surfaces (unpaved runways, safety areas in runway proximity, runway strips, and runway end safety areas). The reason for building this model is to partially substitute frequent and meticulous inspections of an airport movement area comprising the bearing strength evaluation and provide an efficient tool to organize surface maintenance. Since the process of building such a model is complex for a physical model, it is anticipated that it might be addressed by a statistical model instead. Therefore, fuzzy logic (FL) and artificial neural network (ANN) capabilities are investigated and compared with linear regression function (LRF). Large data sets comprising the bearing strength and meteorological characteristics are applied to train the likely model variations to be subsequently compared with the application of standard statistical quantitative parameters. All the models prove that the inclusion of antecedent soil strength as an additional model input has an immense impact on the increase in model accuracy. Although the M7 model out of the ANN group displays the best performance, the M3 model is considered for practical implications being less complicated and having fewer inputs. In general, both the ANN and FL models outperform the LRF models well in all the categories. The FL models perform almost equally as well as the ANN but with slightly decreased accuracy.

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