Motor speed control by using a fuzzy logic model reference adaptive controller

Cheung, J.Y.M.; Cheng, Kun; Kamal, A.

doi:10.1049/cp:19960953

Cited by 9 publications

(7 citation statements)

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“…This, thus, indicates that the adaptation gain must be optimally chosen to eliminate this poor performance. Usually, the adaptation gain is determined heuristically (Cheung 1996;Cheung et al 1996;Keerth and Sathyanarayana 2012;Zadeh 1975). A heuristic approach adequate enough for determining the adaptation gain is the FLC.…”

Section: Figure 1 a Typical Industrial Feedback Control Systemmentioning

confidence: 99%

Fuzzy-Optimized model reference adaptive control of interacting and noninteracting processes based on MIT and Lyapunov rules

Dinakin

Oluseyi

2021

Turkish Journal of Engineering

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Various system parameter variations occur during operations in several existing process industries. These parameter variations result in process shifts, thus, requiring adequate control strategies to compensate for these alterations, which consequently maintain desired system response. A paradigm is the coupled tank systems; in such systems, the level and flow of liquid must be adequately controlled to maintain the reaction equilibrium as well as to avoid spillage or equipment damage. The model reference adaptive control (MRAC) is an adaptive control strategy that creates a control law, subject to an adaptation gain, which causes the system’s plant to continuously track a reference model until a zero tracking error is achieved. The Massachusetts Institute of Technology (MIT) and Lyapunov approaches were used to develop the adaptation mechanism, which is used to adjust the parameters in the control law. Conventionally, a fixed value is adopted as the adaptation gain; however, the adaption gain can also be determined heuristically. The fuzzy logic control was used to optimally determine the value of the adaptation gain, which thus results in the fuzzy-optimized MRAC (FOMRAC) system. Consequently, these schemes were comparatively analysed for the control of the flow and level of liquid in coupled two-tank systems, arranged in noninteracting and interacting fashions. Using MATLAB/Simulink, results depicted that the FOMRAC systems had faster settling times in comparison with the fixed adaptation gain MRAC systems. Overall, the FOMRAC system based on Lyapunov rule yielded the lowest performance indices values. In addition, the scheme completely eliminated the overshoot that resulted from the implementation of the other schemes for the control of the interacting process.

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Section: Figure 1 a Typical Industrial Feedback Control Systemmentioning

confidence: 99%

Fuzzy-Optimized model reference adaptive control of interacting and noninteracting processes based on MIT and Lyapunov rules

Dinakin

Oluseyi

2021

Turkish Journal of Engineering

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“…A fuzzy logic MRAC is applied to control the speed of a DC motor drive in [33] where the adaptation gains are determined by the aid of fuzzy logic approach to maintain satisfactory response irrespective of the magnitude of the inputs. The optimal PI controller gain is obtained by three algorithms (gradient Fig 2. General classification of dc motor speed controller descent, pattern search and simplex search based optimization algorithm) in a closed loop system with parameter uncertainty in [34].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Simulation of Speed Control in DC Motor Drive By Using Fuzzy Control Based on Model Reference Adaptive Control

Shahgholian¹,

Maghsoodi²

2016

CSJ

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Abstract. This paper presents the conventional performance of the model reference adaptive control (MRAC) and the model reference fuzzy adaptive control (MRFAC). The aims of this work are: a) increasing in correspondence of motor speed with defined reference model speed of the system, b) decreasing of noises under load changes and disturbances, and c) increasing of system stability. Thus, model reference adaptive control is applied instead of non-adaptive or conventional control. Also fuzzy controller is used in place of classic controllers like PI controller. The operation of non-adaptive control and the model reference of fuzzy and conventional adaptive control are studied for derive and adjustment of dc motor speed. Then they are compared with each other. The model reference and fuzzy controller are designed based on securing of the entire system stability. Simulation is done with constant and variable loads. The result obtained shows that the adaptive control is more favorite than non-adaptive control. Also fuzzy adaptive control is more satisfactory than conventional adaptive control. The simulations are carried out by using Matlab-Simulink. Keywords

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“…1965 yılında Lotfi A.Zadeh [7] tarafından ileri sürülen bulanık mantık denetleyici (BMD) tekniğidir. BMD sistemin herhangi bir matematiksel modeline ihtiyaç duymadan tamamen uzman kişinin bilgi ve becerisine dayandığı için endüstri de yaygın bir şekilde kullanılmaya başlanmıştır ve oldukça iyi sonuçlar vermektedir [7][8][9][10].…”

Section: Introductionunclassified

“…Uzman kişi denetiminde kesin matematiksel ilişki yerine "sıcak,"az sıcak","ılık", "soğuk" gibi sözel ifadeler kullanılır. Bulanık denetim bu tür bulanık mantık ilişkileri üzerine kurulmuştur [7][8][9]. Bulanık mantık, su arıtma denetimi, metro denetimi, elektronik pazarlar, otomotiv ürünleri, ısı, sıvı, gaz akımı denetimleri, kimyasal ve fiziksel süreç denetimleri gibi bir çok alanda kullanılmaktadır [10][11][12][13][14][15][16][17].…”

Section: Introductionunclassified

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Ganı¹,

Özçalık²,

Açıkgöz³

et al. 2014

A P J Eng Sci

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Doğru akım (DA) motorları kolay denetlenmeleri ve yüksek performans gibi özelliklerinden dolayı endüstride konum ve hız denetimi uygulamalarında sıkça kullanılmaktadır. Doğru akım motorlarının hız denetiminde bulanık mantık esaslı denetim yaygın olarak kullanılmaktadır. Bulanık mantık esaslı denetimin, geleneksel denetim yöntemleri ile elde edilen sonuçlardan daha üstün olduğunu birçok uygulama göstermektedir.Bu çalışmada, doğru akım motorunun hız denetimi için bir PI-bulanık mantık denetleyici tasarlanmıştır.Tasarlanan PI-bulanık mantık denetleyicide tek kural tabanı yerine değişik yapılardaki kural tabanları kullanılarak aynı koşullar altında performanslarının karşılaştırılması yapılmıştır. Benzetim çalışmaları MATLAB/Simulink'te gerçekleştirilmiştir.

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Motor speed control by using a fuzzy logic model reference adaptive controller

Cited by 9 publications

References 0 publications

Fuzzy-Optimized model reference adaptive control of interacting and noninteracting processes based on MIT and Lyapunov rules

Fuzzy-Optimized model reference adaptive control of interacting and noninteracting processes based on MIT and Lyapunov rules

Analysis and Simulation of Speed Control in DC Motor Drive By Using Fuzzy Control Based on Model Reference Adaptive Control

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