Fuzzy optimisation-based control of a solar array system

Simões, Marcelo Godoy; Franceschetti, Nilson Noris

doi:10.1049/ip-epa:19990341

Cited by 93 publications

(4 citation statements)

References 5 publications

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“…The first step in the development of the fuzzy logic controller is doing an examination of a buck-boost converter. The selection of input and output variables for the fuzzy logic controller is contingent upon the unique characteristics of the system under consideration and the intended outcome [19]- [25]. The inputs often used for the controller consist of the voltage error ε(t) and the derivative of the error Δε(t), as stated in ( 12) and ( 13) correspondingly.…”

Section: Fuzzy Logic Controller For Buck-boost Convertermentioning

confidence: 99%

Comparing fuzzy logic and backstepping control for a buck boost converter in electric vehicles

Ennajih,

Allali,

Jarmouni

et al. 2024

IJPEDS

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Significant advances have been made in the control of DC/DC converters, owing to the effective combination of linear and nonlinear approaches. The above-mentioned approaches have greatly improved the efficiency as well as stability of the converter, even when faced with changing conditions. Nevertheless, the emergence of artificial intelligence has introduced new perspectives in the domain. The objective of the article is to examine two separate methodologies for controlling a boost-buck converter: using a nonlinear approach, especially backstepping, and utilizing artificial intelligence through fuzzy logic. The main aim of this work is to illustrate the inherent stability and robustness of fuzzy logic controllers compared to backstepping control in managing effectively various variations and challenges encountered in converter control.

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Section: Fuzzy Logic Controller For Buck-boost Convertermentioning

confidence: 99%

Comparing fuzzy logic and backstepping control for a buck boost converter in electric vehicles

Ennajih,

Allali,

Jarmouni

et al. 2024

IJPEDS

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“…The rules describe the relationships between two input linguistic variables and two output linguistic variables. The fuzzy rules are a series of IF-THEN statements that are usually derived from an expert to achieve optimum results [23]- [25]. By experience and practicing the rules for input linguistic variables are described in Tables 2 and 3.…”

Section: Designing the Rulesmentioning

confidence: 99%

Fuzzy-proportional-integral-derivative-based controller for object tracking in mobile robots

Vuong

Nguyen²

2023

IJECE

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<span lang="EN-US">This paper aims at designing and implementing an intelligent controller for the orientation control of a two-wheeled mobile robot. The controller is designed in LabVIEW and based on analyzed image parameters from cameras. The image program calculates the distance and angle from the camera to the object. The fuzzy controller will get these parameters as crisp input data and send the calculated velocity as crisp output data to the right and left wheel motor for the robot tracking the target object. The results show that the controller gives a fast response and high reliability and quickly carries out data recovery from system faults. The system also works well in the uncertainties of process variables and without mathematical modeling.</span>

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“…Fuzzy control and incremental conductance are another type of algorithms as mentioned in [3] where a fuzzy controller whose rules are directly dependent on the variations obtained in the boost converter and the panel. This directly reflect the change in temperature and intensity of the sun in the atmosphere, on the other side in [4] the authors referred to the incremental conductance algorithm which seeks to find the point where the derivative of the graphic of power vs. voltage of a panel is zero, both algorithms were tested in an artificial environment simulation.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Neural Network Applied to the Maximum Power Point Tracking of a Solar Panel

Mauledoux

Posada

Sánchez

2016

AMM

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This paper shows the design and implementation of a neural network using back propagation method in order to perform the tracking of the maximum power point of a solar panel; this can be achieved by the use of the predictive ability of the network which uses light sensors to perform angular movement of the panel to find the optimum position. Tests were performed both in artificial and real environments; the network tracking sensitivity was tested in the artificial environment and it gave a result of less than 8 degrees of error, on the other hand in terms of voltage an improvement of more than 25% was found on the tracking configuration against an static configuration. As for the real environment testing, the tracking achieved to find the value of maximum power with a difference smaller than 4% of the maximum power measurement obtained.

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Fuzzy optimisation-based control of a solar array system

Cited by 93 publications

References 5 publications

Comparing fuzzy logic and backstepping control for a buck boost converter in electric vehicles

Comparing fuzzy logic and backstepping control for a buck boost converter in electric vehicles

Fuzzy-proportional-integral-derivative-based controller for object tracking in mobile robots

Design and Implementation of a Neural Network Applied to the Maximum Power Point Tracking of a Solar Panel

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