Analysis of speed control in DC motor drive by using fuzzy control based on model reference adaptive control

Shahgholian, Ghazanfar; Maghsoodi, Mojtaba; Mahdavian, M.; Janghorbani, Mohammadreza; Azadeh, Mansoureh; Farazpey, Saeed

doi:10.1109/ecticon.2016.7561239

Cited by 16 publications

(8 citation statements)

References 19 publications

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“…Shahgholian et al, in their study, compared the performance of a conventional model reference adaptive control and a model reference fuzzy adaptive control [3]. Layne and Passino examined the use of a fuzzy logic-based adaptive supervisory learning system to maintain adequate performance of a cargo ship autopilot when there are process disruptions or variations [4] [5].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Controller Design for Electric Vehicle(EV) Speed Control Using An Inverse Model Based Reference Model Learning Algorithm

Bulut¹

2023

Preprint

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The adaptation mechanism, which adjusts the controller coefficients according to the parameter changes in the system, ensures that the controller is adaptable. Fuzzy logic can be used to calculate the gain coefficients of the controller in the system by using the adaptive fuzzy method instead of a traditional algorithm for the adaptation mechanism. Normally, the rules of a fuzzy controller system are derived from the system's internal structure and system behavior using expert knowledge that has experienced the system. However, it is not possible to derive fuzzy rules based on expert human knowledge for all systems in this way. It is necessary to use different methods to derive fuzzy rules in highly variable behavior and nonlinear systems. In this study, an adaptive fuzzy controller design for dc motor was made using a learning-based reference model learning algorithm using fuzzy inverse model; It has been shown that it is applicable for dc motors with the results obtained. Simulation of the designed system was carried out using the Matlab program, and the behavior of the system was investigated by using constant and variable loads. The results showed that it is satisfactory to drive a dc motor with adaptive fuzzy controller in terms of system stability.

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Section: Introductionmentioning

confidence: 99%

Intelligent Controller Design for Electric Vehicle(EV) Speed Control Using An Inverse Model Based Reference Model Learning Algorithm

Bulut¹

2023

Preprint

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“…Bulanık kümeler kavramı ilk kez 1965 yılında Amerikalı bilim adamı Zadeh tarafından önerilmiş ve günümüze kadar kontrol alanında birçok uygulama yapılmıştır [6]. Shahgholian ve diğerleri, çalışmalarında, geleneksel model referans uyarlamalı denetleyici ile model referans bulanık uyarlamalı denetleyicinin performansını karşılaştırmışlardır [7].…”

Section: Gi̇ri̇ş (Introduction)unclassified

Bulanık Ters Model Kullanılarak Doğru Akım Motor Sürücüsü için Referans Model Temelli Uyarlanabilir Bulanık Denetleyici

Bulut

2023

Politeknik Dergisi

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Sistemdeki parametre değişimlerine göre denetleyici katsayılarını ayarlayan uyarlama mekanizması, denetleyicinin uyarlanabilir olmasını sağlamaktadır. Uyarlama mekanizması için geleneksel bir algoritma yerine uyarlanabilir bulanık metodu kullanılarak, sistemdeki denetleyicinin kazanç katsayıların hesaplanması için bulanık mantık kullanılabilir. Normalde bir bulanık denetleyici sistemine ait kurallar, sistemi deneyimlemiş olan uzman bilgisi kullanılarak sistemin içyapısından ve sistem davranışlarından çıkarılmaktadır. Ancak tüm sistemler için bu şekilde uzman insan bilgisine dayanan bulanık kuralların çıkarılması mümkün değildir. Çok değişken davranış gösteren ve doğrusal olmayan sistemlerde bulanık kurallarının çıkarılması için farklı yöntemlerin kullanılması ihtiyacı ortaya çıkmaktadır. Bu çalışmada, bulanık ters model kullanılarak öğrenmeye dayalı referans model öğrenme algoritması kullanılarak dc motor için uyarlanabilir bulanık denetleyici tasarımı yapılmış; elde edilen sonuçlar ile dc motor için uygulanabilir olduğu gösterilmiştir. Tasarlanan sistemin benzetimi Matlab programı kullanılarak gerçekleştirilmiş, sabit ve değişken yükler kullanılarak sistemin davranışı incelenmiştir. Elde edilen sonuçlar, sistem kararlılığı açısından uyarlanabilir bulanık denetleyici ile dc motoru sürmek için tatmin edici olduğunu göstermiştir.

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“…Permanent magnet direct-current (DC) motors exhibit good servo performance, which makes them a popular choice as actuators in various household, automotive, and robotic applications [1]. High-precision machines require DC motors to demonstrate superior error-tracking capability and susceptibility to noise.…”

Section: Introductionmentioning

confidence: 99%

EKF-based self-regulation of an adaptive nonlinear PI speed controller for a DC motor

Saleem¹,

Omer²

2017

Turk J Elec Eng & Comp Sci

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This paper presents a robust adaptive nonlinear proportional-integral (ANPI) scheme to control the speed of a direct-current (DC) motor. Unlike proportional-integral-derivative (PID) controllers, PI controllers have a simpler structure and they deliver effective control effort. However, due to inadequate controller gains, they are often unable to simultaneously improve the transient as well as the steady-state response of the system. A nonlinear PI (NPI) controller alleviates these issues and delivers a good response. In this research, the proportional and integral gains of the NPI controller are dynamically modulated via a nonlinear sigmoidal function (SiF) of the error dynamics of the motor's angular speed. The variation rates of these functions are manually tuned via trial-and-error method. These rates are also dynamically updated via an extended Kalman filter (EKF)-based adaptation mechanism. The performances of a linear PI controller, an NPI controller having fixed variation rates, and an NPI controller equipped with the EKFbased self-regulated SiFs are tested and compared in real time. The experimental results are analyzed to validate the effectiveness of the proposed ANPI controller in optimizing DC motor speed control.

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Analysis of speed control in DC motor drive by using fuzzy control based on model reference adaptive control

Cited by 16 publications

References 19 publications

Intelligent Controller Design for Electric Vehicle(EV) Speed Control Using An Inverse Model Based Reference Model Learning Algorithm

Intelligent Controller Design for Electric Vehicle(EV) Speed Control Using An Inverse Model Based Reference Model Learning Algorithm

Bulanık Ters Model Kullanılarak Doğru Akım Motor Sürücüsü için Referans Model Temelli Uyarlanabilir Bulanık Denetleyici

EKF-based self-regulation of an adaptive nonlinear PI speed controller for a DC motor

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