Adaptive fuzzy logic controller for DC–DC converters

Elmas, Çetin; Deperlioğlu, Ömer; Sayan, H. Hüseyin

doi:10.1016/j.eswa.2007.11.029

Cited by 108 publications

(46 citation statements)

References 15 publications

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“…Estes controladores se caracterizam pela capacidade de modelar processos a partir de regras fuzzy do tipo "se-então", extraídas comumente do operador da planta. As abordagens clássicas evocam o uso de Sistemas de Inferência Fuzzy (SIF) do tipo Mamdani (Elmas et al, 2009;Córdon, 2011). Contudo, com o objetivo de atingir maior precisão e agilidade, controladores fuzzy do tipo Takagi-SugenoKang tem sido recentemente explorados com maior ênfase (Jang et al, 1997;Precup e Hellendoorn, 2011).…”

Section: Introductionunclassified

GPF-control: um modelo fuzzy-genético para problemas de controle

Koshiyama

Vellasco

Tanscheit

2013

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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Section: Introductionunclassified

GPF-control: um modelo fuzzy-genético para problemas de controle

Koshiyama

Vellasco

Tanscheit

2013

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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“…The second block is devoted to inference rules, while the last block is the Defuzzification for returning to the real domain. This last operation uses the center of mass to determine the value of the output [16]. The FLC block diagram to control DC-DC power converter in MPPT system of PVS is presented in Fig.5.…”

Section: Fuzzy Logic Controllermentioning

confidence: 99%

“…By using this method the designer of an FLC assigns only the number of elements of term set and shrinking factor. In fact, the shrinking-span membership functions [16] (SSMF) is constructing membership functions method for FLC which, in compare to [21], generates a series of orderly arranged membership functions A(x i )s in the FLC for a linguistic variable across its universe of discourse. For example [16], widely used trapezoidal family SSMF is showing on Fig.6 for the membership number of linguistic variables m=3, shrinking factors s=0.65 and overlapping b=1.…”

Section: The Output Of the Flc Is A Change In Duty Ratio (Du(k))mentioning

confidence: 99%

“…In fact, the shrinking-span membership functions [16] (SSMF) is constructing membership functions method for FLC which, in compare to [21], generates a series of orderly arranged membership functions A(x i )s in the FLC for a linguistic variable across its universe of discourse. For example [16], widely used trapezoidal family SSMF is showing on Fig.6 for the membership number of linguistic variables m=3, shrinking factors s=0.65 and overlapping b=1. In case when shrinking factor is chosen one, the membership functions have equal span .Using various shrinking factors to the same linguistic variable, different membership function obtained to examine which is the most suitable for a specific application process.…”

Section: The Output Of the Flc Is A Change In Duty Ratio (Du(k))mentioning

confidence: 99%

“…Furthermore, corresponding authors made their effort in the design of Fuzzy Logic Controllers and demonstrated some difficulties in the selection of optimized membership functions and fuzzy rule base, which is traditionally achieved by a tedious trial-and-error process. This paper is a synthesis of works by [ 10,15,16,21,22 ] and introduces a systematic approach to construct FLC for DC-DC converters as a part of Maximum Power Point Tracing system of Photovoltaic station to adapt to photovoltaic modules under varying operating conditions and the nonlinear properties of DC-DC power converters. The modified FLC (MFLC) optimizes membership functions and rule base of the FLC were obtained from training data in the pattern file.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A study of maximum power point tracking algorithm for photovoltaic systems using a fuzzy logic controller

Elzein¹,

Petranko²

2014

Automation, Mechanical and Electrical Engineering

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Solar panels have a nonlinear voltage-current characteristic, with a distinct maximum power point (MPP), which depends on the environmental factors, such as temperature and irradiation. In order to continuously harvest maximum power from the solar panels, they have to operate at their MPP despite the inevitable changes in the environment. This is why the controllers of all solar power electronic converters employ some method for maximum power point tracking (MPPT). Over the past years many MPPT techniques have been published and based on that the main paper's objective is to analyze one of the most promising MPPT control algorithms: fuzzy logic controller. I. IntroductionAccording to the realization of high efficiency and low cost photovoltaic (PV) modules, interest in photovoltaic power generation system has increased over the past decade as a clean and infinite energy [1]. The PV modules have maximum operating points corresponding to the surrounding condition such as intensity of the sunlight, the temperature of the PV modules, cell area, and load. When solar energy is used as a power source, the output power has to be maximized by improving the efficiency of the power conditioning equipment used and implementing an adaptive power controller that automatically tracks the system to the point of maximum power delivered from the solar panel under all conditions.

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Real‐time implementation of Chebyshev neural adaptive controller for boost converter

Govindharaj

Mariappan

2020

Int Trans Electr Energ Syst

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Summary An indirect approach is employed to track the desired output voltage of the boost converter by controlling the inductor current in the proposed adaptive backstepping Chebyshev neural network controller, because of the non‐minimum phase nature of the converter. The computational complexity of the neural network is avoided by the use of Chebyshev polynomials as the basis function. The online weight update of the Chebyshev neural network is designed for the closed‐loop system based on the Lyapunov stability analysis to obtain an asymptotically stable system. In the proposed work, the required duty cycle is obtained by a novel method of solving the quadratic equation of the control function instead of the first derivative of the duty cycle to get the desired output voltage from the Lyapunov control function. Detailed analyses of simulations are carried out for a wide range of variations in the set point and load, and the results are compared with that of backstepping and PID controllers. The proposed controller exhibits superior performance than other controllers for the uncertainties caused by disturbances. To ensure its suitability in real time, a prototype is designed for the proposed controller, and the obtained results are compared with that of backstepping and PID controllers. Investigation of experimental results confirms the adaptability of the proposed control scheme as it exhibits accurate and fast response irrespective of disturbances acting upon it.

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Adaptive fuzzy logic controller for DC–DC converters

Cited by 108 publications

References 15 publications

GPF-control: um modelo fuzzy-genético para problemas de controle

GPF-control: um modelo fuzzy-genético para problemas de controle

A study of maximum power point tracking algorithm for photovoltaic systems using a fuzzy logic controller

Real‐time implementation of Chebyshev neural adaptive controller for boost converter

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