Proceedings ArticleDOI

# Comparison of fuzzy and MPC based buck converter

01 Dec 2014-pp 1-6
TL;DR: In this article, the authors presented the design and construction of a Model Predictive Controller (MPC) for a Pulse Width Modulation (PWM) based buck converter, working in Continuous Conduction Mode (CCM).
Abstract: This article presents the design and construction of Model Predictive Controller (MPC) for a Pulse Width Modulation (PWM) based buck converter, working in Continuous Conduction Mode (CCM). The converter operates at a switching frequency of 100 KHz. The buck converter is mathematically modeled and implemented in matlab simulink The open loop response of the buck converter is used to obtain the transfer function (second order) of the model, which in turn is used to design the MPC controller. This MPC is used to control the output voltage of the buck converter and it has been found that the output voltage is maintained constant accurately for dynamic load, and the peak overshoot is reduced drastically. The output voltage obtained with MPC controller is then compared with the output voltage obtained from fuzzy based buck converter and it has been observed that the MPC gives better output than the fuzzy controller.
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Journal ArticleDOI
TL;DR: In this paper, a DC-DC boost converter is controlled using switching function extraction method based on Lyapunov stability theorem, which makes the converter system remain stable in all operational conditions.
Abstract: Objectives: To design a control system for DC-DC boost converter based on lyapunov stability theorem that fulfills all control purposes. Methods/Statistical Analysis: A DC-DC boost converter is controlled using switching function extraction method based on lyapunov stability theorem. Switching function extraction is a proper method to control switching devices such as DC-DC converters. At first state-equations of the system are obtained from large signal averaged model of the system. After that, fundamentals of lyapunov theorem are applied to state equations that lead to the calculation of switching function by which the converter is controlled. Findings: Using the presented method in this paper based on lyapunov theorem makes the converter system remain stable in all operational conditions. Also, it is shown that using proposed system for controlling DC-DC converter; state variable tracks its reference with an acceptable response. The current controller is also compared with conventional PI controller and it is observed that lyapunov based controller operates better in many ways. By using this controller stability of the system is guaranteed and when there is a sudden change in the reference of state variable, the state variable tracks down and corresponds with the new value. Application/Improvements: This controller can be used in all kinds of DC-DC, DC-AC and other kinds of converters. It is also applicable to all switching devices such as switching power sources.

8 citations

### Cites background from "Comparison of fuzzy and MPC based b..."

• ...Merging Equation (12) and inequality (13): max ( ) || || T X PX P x λ ≤ (14) Multiplying both sides of (21) with max 1 λ − :...

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• ...min max ( ) || || ( ) || || P x V P x λ λ ≤ ≤ (13) In which λmin and λmax are minimum and maximum eigen values of P matrix respectively....

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• ...According to (10), (13) and (15), the DC-DC boost converter is globally and uniformly stable if the derivative of function V satisfies following equation:...

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Proceedings ArticleDOI
01 Jul 2018
TL;DR: The experimental and simulation results demonstrate the effectiveness of the presented analysis, design, and implementation of managed DC voltage sources.
Abstract: This paper introduces the reproduction of managed DC voltage sources. The DC source primary supply is a sun based PV demonstrate which creates a flimsy yield voltage. The model is done with the end goal that the info parameters are the light and the temperature and the yield is the unsteady voltage. The yield current is an imperative parameter to accomplish exact yield voltage, that current is estimated and encouraged to the contribution of the model to be utilized for condition assessment. Voltage direction i.e regulation is expected to make the created voltage usable. A straightforward buck converter with PID controller used to manage the yield voltage. SIMULINK is utilized to mimic the general framework. Reenactment result is given to confirm the operation of the model. The experimental and simulation results demonstrate the effectiveness of the presented analysis, design, and implementation

2 citations

### Cites background from "Comparison of fuzzy and MPC based b..."

• ...PV cell comprises of a present source subject to light(Irradiation) temperature, a diode that behaviours turn or reverses around immersion present, forward arrangement protection of the cell [5-6]....

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##### References
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Journal ArticleDOI
TL;DR: It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems and the models introduced are practically feasible.
Abstract: It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis is on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are interconnected in novel configurations, and hence there is a real need to study them in a unified fashion. Simulation results reveal that the identification and adaptive control schemes suggested are practically feasible. Basic concepts and definitions are introduced throughout, and theoretical questions that have to be addressed are also described. >

7,692 citations

### "Comparison of fuzzy and MPC based b..." refers background in this paper

• ...The stability of non-linear systems can be established only on system-by-system basis and hence the design procedures for a controller that meets the stability, robustness and good dynamical response are not available for large classes of such systems [6]....

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Journal ArticleDOI
Manfred Morari
TL;DR: In this article, a theoretical basis for model predictive control (MPC) has started to emerge and many practical problems like control objective prioritization and symptom-aided diagnosis can be integrated into the MPC framework by expanding the problem formulation to include integer variables yielding a mixed-integer quadratic or linear program.

2,320 citations

### "Comparison of fuzzy and MPC based b..." refers background or methods in this paper

• ...Model Predictive Control (MPC) allows us to address problems like feasibility, stability and performance in a rigorous manner [7]....

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• ...The most popular method is the extended Kalman filter, which simply relinearizes the non-linear model at each time step and updates the gain matrix and the co-variance matrix on the basis of linear filtering theory [7]....

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Journal ArticleDOI

TL;DR: A simple classification of the most important types of predictive control is introduced, and each one of them is explained including some application examples.
Abstract: Predictive control is a very wide class of controllers that have found rather recent application in the control of power converters. Research on this topic has been increased in the last years due to the possibilities of today's microprocessors used for the control. This paper presents the application of different predictive control methods to power electronics and drives. A simple classification of the most important types of predictive control is introduced, and each one of them is explained including some application examples. Predictive control presents several advantages that make it suitable for the control of power converters and drives. The different control schemes and applications presented in this paper illustrate the effectiveness and flexibility of predictive control.

1,389 citations

Proceedings ArticleDOI
18 Nov 2008
TL;DR: In this article, a Powerpoint presentation on predictive control in power electronics and drives is presented, where the areas discussed include predictive control, power electronics, power drive, cascaded control structure, nonlinear control system, switching system, etc.
Abstract: The article consists of a Powerpoint presentation on predictive control in power electronics and drives. The areas discussed include: predictive control; power electronics; power drive; cascaded control structure; nonlinear control system; switching system; etc. etc.

1,073 citations

### "Comparison of fuzzy and MPC based b..." refers background in this paper

• ...The Model Predictive Controller (MPC) also known as the receding horizon control technique is one of the most successful controller for practical applications [11] and also is one of the emerging research field for control requirements....

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Proceedings ArticleDOI
08 Oct 1995
TL;DR: In this paper, a comparative evaluation of the proportional-integral, sliding mode and fuzzy logic controllers for applications to power converters is presented, and the mismatch between the characteristics which lead to varying performance is outlined.
Abstract: This paper presents a comparative evaluation of the proportional-integral, sliding mode and fuzzy logic controllers for applications to power converters. The mismatch between the characteristics which lead to varying performance is outlined. This paper also demonstrates certain similarities of both the fuzzy logic controller and sliding mode controller. Sensitivity of these controllers to supply voltage disturbances and load disturbances is studied and results are presented.

350 citations