Speed Control of DC Motor Using Interval Type-2 Fuzzy Logic Controller

Açıkgöz, Hakan

doi:10.18201/ijisae.2018644777

Cited by 16 publications

(10 citation statements)

References 10 publications

(13 reference statements)

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“…Compared to combined classical-AI controllers [18] and AI controllers with static tuning parameters [19], the proposed TSK-PSO-FLC is a dynamic controller (where it could tune its parameters by it-self according to the Ek and dEk). Moreover, compared to traditional DC motor driving techniques [20] the FOC mechanism could deliver the exact resultant magnetic force amount to the PM-BLDC motor (the generated electric torque "Te") which was decided through the proposed dynamic AI controller. Therefore, in addition to the simulation test results, the observed test results through the implemented hardware setup show that by combining the proposed dynamic AI control mechanism with the FOC mechanism have been significantly enhanced the performance of the PM-BLDC motor.…”

Section: Analysis Of the Simulation Test Resultsmentioning

confidence: 99%

ADynamic AI Controllerfor aField-Oriented ControlledBLDC Motorto Achieve the Desired AngularVelocity and Torque

Jayetileke

Mel

Ratnayake

2019

ijisae

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Compared to traditional motor controlling techniques, modern AI controllers have many advantages. However, most of the developed FOC mechanisms are based on classical controlling techniques such as PID controllers, hybrid AI-classical controllers and model reference controllers. All these traditional controllers are based on sophisticated mathematical models (system transfer functions). These traditional controllers are unable to tune its system parameters by itself to adapt according to the non-linear variations of actual speed and torque of the motor. This paper discussed, a novel scheme of metaheuristic adaptive fuzzy logic-particle swarm optimization control mechanism to optimize the speed regulation of electric current space vector-controlled BLDC motor. Therefore, dynamic TSK-PSO-FLC was investigated. The dynamic behaviour of the proposed controller enables it to optimize its tuning parameters by itself under non-linear load and speed varying conditions to track the desired angular speed and the torque trajectories. This is a part of the designed and developed dynamic AI controller, for stability and traction control of an all-wheel-drive electric rover. Therefore, initially, the performance of the proposed controller has been tested on a simulation environment (MATLAB Simulink model) for one wheel. Finally, the identified dynamic parameters through the Simulink model (the sensored BLDC motor and the proposed AI controller) were utilized to test the performance of the developed TSK-PSO-FLC, while it is tracking a given desired speed trajectory of the BLDC motor (sensored, 3-ph and 250 W) in real-time operation. Simulated test results are analyzed and compared with the observed test results through the developed hardware model in addition to the newly published research work. The angular speed of 2500 rpm within a 500 N m torque have been taken into consideration as a generalized condition. It was noticed that the percentage overshoot (Mp%), settling time (Ts) and the steady-state error (Ess) is 0.501, 731.455 μs and 1.22 respectively. Therefore, compared to classical control based FOC mechanisms, the analyzed test results of the proposed control mechanism is showing that it has been optimized and enhanced the speed regulation performance of the BLDC motor significantly while increasing the frequency of the desired input trajectory up to 2 kHz.

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Section: Analysis Of the Simulation Test Resultsmentioning

confidence: 99%

ADynamic AI Controllerfor aField-Oriented ControlledBLDC Motorto Achieve the Desired AngularVelocity and Torque

Jayetileke

Mel

Ratnayake

2019

ijisae

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“…Tüm birincil üyelik fonksiyonları arasında kalan alan Belirsizliğin Ayak İzi (FOU) olarak ifade edilmektedir [14][15][16]. Şekil 2'de gösterilen Gauss üyelik fonksiyonları arasında kalan diğer bir deyişle alt ve üst üyelik fonksiyonları ile sınırlandırılmış bu alan aşağıdaki gibi tanımlanabilir: [10][11]16]. Bulanıklaştırıcı, girişleri (x=x 1 ,…,x p ) daha önce tanımlanan üyelik fonksiyonlarına net bir nokta olarak eşler.…”

Section: Aralikli Ti̇p-2 Bulanik Mantik Denetleyi̇ci̇ Yapisinin Tasarimiunclassified

“…T1BMD'ler birçok endüstriyel uygulamalarda sıklıkla tercih edilmektedir. Bilindiği gibi T1BMD'lerde uzman bilgi ve birikimi çok önemlidir [10][11]. Bu tip denetleyicilerde kullanılan kümeler [0,1] arasında net değere sahip oldukları için kurallarda meydana gelen belirsizliklerle baş etmede yeterli özelliklere sahip değildirler.…”

Section: Igi̇ri̇şunclassified

Aralıklı Tip-2 Bulanık Mantık Denetleyici Tabanlı Yükselten Tip DA-DA Çevirici Yapısının Denetimi ve Deneysel Analizi

Açıkgöz¹,

Keçecioğlu

2020

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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Bu çalışmada, yükselten tip DA-DA çeviricinin çıkış geriliminin denetimi için Aralıklı Tip-2 Bulanık Mantık Denetleyici (AT2BMD) yapısı önerilmiştir. Bu denetleyici yapısı ile yük değişimine karşı DA-DA çeviricinin dinamik cevabının iyileştirilmesi amaçlanmıştır. Bu amaçla ilk olarak yükselten tip DA-DA çevirici yapısı deneysel olarak geliştirilmiştir. Daha sonra önerilen denetleyici yapısının performansının ve güvenilirliğinin incelenmesi için birbirinden farklı iki deneysel çalışma durumu oluşturulmuştur. AT2BMD ile Tip-1 BMD (T1BMD) bu çalışma durumları altında karşılaştırılmıştır. Bu denetleyici yapılarının deneysel çalışmalara uygulanabilmesi için dSPACE DS1104 denetleyici kartı kullanılmıştır. Elde edilen sonuçlara göre önerilen denetleyici yapısının her iki deneysel çalışmada da T1BMD'den daha tatmin edici dinamik cevaplar sağladığı açıkça gözlenmiştir.

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“…In works Kumar and Patnaik (2015), Aggarwal et al (2015), and Salim (2015) was presented the speed adjustment algorithms with proportional-integrative (PI) controller or Fuzzy Logic controller that was implemented in LabVIEW for DC motors or servomotors. A comparison of the results obtained through the Fuzzy Logic controller versus the conventional PI controller for controlling the speed of the D.C. motors or induction motors has been presented in the works of Nagarajan et al (2019), Acikgoz (2018), Shaija and Daniel (2016), Rajpoot et al (2018) and Usha and Subramani (2018). In the works of Asija (2010), Faisal and Paliwal (2018), Kalhoodashti and Shahbazian (2011), and Manjunatha (2017); the speed adjustment algorithms with PI controllers and Fuzzy Logic controllers have been presented that were simulated in MATLAB-SIMULINK for three-phase asynchronous motors.…”

Section: Introductionmentioning

confidence: 99%

Speed control of the asynchronous motor using LabVIEW

Livinti¹

2021

Int. j. adv. appl. sci.

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This paper was presented a comparative study on the methods of adjusting the speed of a three-phase asynchronous motor with a rotor in a short circuit. For the same structure of the experimental stand used, two programs were created, implemented, and validated in LabVIEW. For the first method, the program in LabVIEW was made with the PI (proportional-integrative) controller and for the second method, the program in LabVIEW was made with the Fuzzy Logic controller. Following the analysis of the resulting graphs, it was found that the speed control system made with the fuzzy logic controller ensures an increase in its performance compared to the speed control system made with the conventional PI type controller. The indicial responses of the adjustment system of the three-phase asynchronous motor speed with PI controller or Fuzzy Logic controller have been determined in real-time by means of the experimental stand. The override of the speed adjustment system is decreased from the value of 26.9% corresponding to the PI controller to the value of 2.3% corresponding to the Fuzzy Logic controller and the duration of the transient time is decreased from the value of 2.2 s related to the PI controller to the value of 0.5 s, related to the Fuzzy Logic controller. By using the Fuzzy Logic controller, the amount of electrical energy required to supply the electric drive system made with a three-phase asynchronous motor will be reduced. This three-phase asynchronous motor speed adjustment algorithm can be implemented for other electric drive systems from different industrial applications.

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Speed Control of DC Motor Using Interval Type-2 Fuzzy Logic Controller

Cited by 16 publications

References 10 publications

ADynamic AI Controllerfor aField-Oriented ControlledBLDC Motorto Achieve the Desired AngularVelocity and Torque

ADynamic AI Controllerfor aField-Oriented ControlledBLDC Motorto Achieve the Desired AngularVelocity and Torque

Aralıklı Tip-2 Bulanık Mantık Denetleyici Tabanlı Yükselten Tip DA-DA Çevirici Yapısının Denetimi ve Deneysel Analizi

Speed control of the asynchronous motor using LabVIEW

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