Fuzzy logic controller (FLC): Application to control DC-DC buck converter

Bendaoud, Kaoutar; Krit, Salah-ddine; Kabrane, M.; Ouadani, H.; Elaskri, Mohamed; Karimi, K.J.; Elbousty, H.; Elmaimouni, L.

doi:10.1109/icemis.2017.8272980

Cited by 23 publications

(9 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The IM is the kernel of the FLC, which is capable of simulating human decision making based on fuzzy concepts and inferring fuzzy control actions using fuzzy relations and rules of inference. The DI makes conclusions from the IM and gives an output, i.e., input for the plant [41,42]. 4 presents the model of an FC with a fuzzy logic controller in MATLAB/Simulink.…”

Section: Flcmentioning

confidence: 99%

Artificial Intelligence-Based Controller for DC-DC Flyback Converter

et al. 2019

View full text Add to dashboard Cite

This paper presents an intelligent voltage controller designed on the basis of an adaptive neuro-fuzzy inference system (ANFIS) for a flyback converter (FC) working in continuous conduction mode (CCM). The union of fuzzy logic (FL) and adaptive neural networks (ANN) makes ANFIS more robust against model parameters’ uncertainties and perturbations in input voltage or load current. ANFIS inherits the advantages of structured knowledge representation from FL and learning capability from NN. Comparative analysis showed that the ANFIS controller offers not only the superior transient response characteristics, but also excellent steady-state characteristics compared to those of the FL controller (FLC) and proportional–integral–derivative (PID) controllers, thus validating its superiority over these traditional controllers. For this purpose, MATLAB/Simulink environment-based simulation results are presented for validation of the proposed converter compensated system under all operating conditions.

show abstract

Section: Flcmentioning

confidence: 99%

Artificial Intelligence-Based Controller for DC-DC Flyback Converter

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In this context there are different kind of controls which have been implemented in both converters and inverters. Particularly, the direct current converter control involves a diversity of techniques [10] - [12], namely, direct pole placement [13]; voltage control mode [14]; current control mode [15], [16]; PID control [17]; classical half cycle posicast control [18]; hybrid posicast control [19]; sliding control mode [20]; fuzzy logic control [21], [22]; neural network-based control [23]; predictive model control [24]; state space modeling control and passivity-based control [25], [26]; and intelligent control, among others. These techniques allow achieving and/or satisfying the demands of problems in specific systems.…”

Section: Introductionmentioning

confidence: 99%

Voltage Regulation in Second-Order Dc-Dc Converters Via the Inverse Optimal Control Design with Proportional-Integral Action

Gómez-Chitiva

Escalante-Sarrias

Montoya

2022

TecnoL.

View full text Add to dashboard Cite

This article addresses the problem regarding power regulation in classical DC-DC second-order converters by means of a nonlinear control technique based on inverse optimal control theory. There are few papers that describe inverse optimal control for DC-DC converters in the literature. Therefore, this study constitutes a contribution to the state of the art on nonlinear control techniques for DC-DC converters. In this vein, the main objective of this research was to implement inverse optimal control theory with integral action to the typical DC-DC conversion topologies for power regulation, regardless of the load variations and the application. The converter topologies analyzed were: (i) Buck; (ii) Boost; (iii) Buck-Boost; and (iv) Non-Inverting Buck-Boost. A dynamical model was proposed as a function of the state variable error, which helped to demonstrate that the inverse optimal control law with proportional-integral action implemented in the different converters ensures stability in each closed-loop operation via Lyapunov’s theorem. Numerical validations were carried out by means of simulations in the PSIM software, comparing the established control law, the passivity-based PI control law, and an open-loop control. As a conclusion, it was possible to determine that the proposed model is easier to implement and has a better dynamical behavior than the PI-PBC, ensuring asymptotic stability from the closed-loop control design.

show abstract

“…Among the switching control methods for buck converters that have been presented in literature, proportional–integral–derivative (PID), fuzzy logic and neural network controllers are commonly seen. The PID controller is implemented in this paper in series with a pulse‐width modulator (PWM) generator that adjusts the gain of the MOSFET [5, 6]. A fuzzy logic controller is implemented as a method of control that utilises various membership functions in order to generate a set of values that correspond to the inputs given [1, 7].…”

Section: Introductionmentioning

confidence: 99%

“…An advanced controller system of buck converters could result in a more efficient and cost-effective electronic application for the buck controller as well as an easier way to control the outputs of systems of all classifications. Literature regarding the control of buck converter systems has focused on linear control methods, PID control, fuzzy logic control or other predictive mechanisms that operate on the input to a PWM generator in order to control the gain of a MOSFET [5][6][7][8][9][10][11]. Modern switching control methods are still under research.…”

Section: Introductionmentioning

confidence: 99%

Dynamic switching control of buck converters using unsupervised machine learning methods

Abegaz

2020

J. eng.

View full text Add to dashboard Cite

Fuzzy logic controller (FLC): Application to control DC-DC buck converter

Cited by 23 publications

References 1 publication

Artificial Intelligence-Based Controller for DC-DC Flyback Converter

Artificial Intelligence-Based Controller for DC-DC Flyback Converter

Voltage Regulation in Second-Order Dc-Dc Converters Via the Inverse Optimal Control Design with Proportional-Integral Action

Dynamic switching control of buck converters using unsupervised machine learning methods

Contact Info

Product

Resources

About