Microcontroller based fuzzy logic technique for dc-dc converter

Nagaraj, R.; Mayurappriyan, P. S.; Jerome, Jovitha

doi:10.1109/iicpe.2006.4685397

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…In such cases, hardware implementations may be a better option [15]. This approach includes programmable devices such as microcontrollers (μC) [16] and Field Programmable Gate Array (FPGA) devices [15,17,18]. They are also reported full custom transistor level systems of this type (ASIC), analog [19][20][21], digital [22], or mixed [23].…”

Section: Introductionmentioning

confidence: 99%

Parallel, asynchronous, fuzzy logic systems realized in CMOS technology

Talaśka

2019

Adv Comput Math

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Fuzzy systems play an important role in many industrial applications. Depending on the application, they can be implemented using different techniques and technologies. Software implementations are the most popular, which results from the ease of such implementations. This approach facilitates modifications and testing. On the other hand, such realizations are usually not convenient when high data rate, low cost per unit, and large miniaturization are required. For this reason, we propose efficient, fully digital, parallel, and asynchronous (clock-less) fuzzy logic (FL) systems suitable for the implementation as ultra low-power-specific integrated circuits (ASICs). On the basis of our former work, in which single FL operators were proposed, here we demonstrate how to build larger structures, composed of many operators of this type. As an example, we consider Lukasiewicz neural networks (LNN) that are fully composed of selected FL operators. In this work, we propose FL OR, and AND Lukasiewicz neurons, which are based on bounded sum and bounded product FL operators. In the comparison with former analog implementations of such LNNs, digital realization, presented in this work, offers important advantages. The neurons have been designed in the CMOS 130nm technology and thoroughly verified by means of the corner analysis in the HSpice environment. The only observed influence of particular combinations on the process, voltage, and temperature parameters was on delays and power dissipation, while from the logical point of view, the system always worked properly. This shows that even larger FL systems may be implemented in this way. Keywords Fuzzy logic systems • FL operators • FL neural networks • Asynchronous circuits • Parallel circuits • CMOS implementation

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Section: Introductionmentioning

confidence: 99%

Parallel, asynchronous, fuzzy logic systems realized in CMOS technology

Talaśka

2019

Adv Comput Math

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“…On the other hand, the use of fuzzy logic theory in designing speed controller requires, however, the knowledge of human expert to create an algorithm that agrees with human expertise and thinking [5]. Recently, many papers were published that involved in demonstrating the power of the FLC as a decision making in designing different electrical drives (see for example [6], [7], [8], and [9]).…”

Section: Introductionmentioning

confidence: 99%

Combined Armature and Field Fuzzy Speed Control of a DC Motor for Efficiency Enhancement

M.Adday¹,

.Othman²

2012

(AREJ)

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This paper presents the design and implementation of fuzzy logic controller (FLC) to improve the efficiency of a separately excited dc motor .The improvement of motor efficiency is achieved by controlling both armature voltage and field voltage simultaneously which are decided by the FLC. DC-DC converters (chopper Buck) are utilized to control armature voltage and field voltage. Design and MATLAB simulation are presented together with implementation of fuzzy logic controller using microcontroller type (MC9S12DP256B).The results show that the efficiency of the DC motor is clearly increased in light and medium load torques in comparison to the armature fuzzy speed controller.

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“…In order to avoid the need for expert knowledge, some neural-network approaches [16][17][18] and also the genetic algorithm (GA) approaches are suggested in [19,20]. Implementations of FLC algorithms within DC-DC converters for control of inductor current and output voltage are described in [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy-logic state controller for DC–DC step-down converter

et al. 2017

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This paper deals with the implementation of a control algorithm based on a state controller with an adaptation of the state controller gains by using the fuzzy-logic approach. Adjustable state controller gains cause that the static error can be reduced arbitrarily for a system with variable parameters as is the DC-DC step-down converter for power supply systems. Fuzzy sets are tuned offline by a genetic algorithm using fuzzy-logic and a genetic toolbox in MATLAB. Fuzzification and defuzzification algorithms are implemented in real time within a Field Programmable Gate Array circuit. The whole control algorithm is performed within a sampling time of 5.33 µs. Operation of the controller is verified experimentally.

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Microcontroller based fuzzy logic technique for dc-dc converter

Cited by 7 publications

References 15 publications

Parallel, asynchronous, fuzzy logic systems realized in CMOS technology

Parallel, asynchronous, fuzzy logic systems realized in CMOS technology

Combined Armature and Field Fuzzy Speed Control of a DC Motor for Efficiency Enhancement

Adaptive fuzzy-logic state controller for DC–DC step-down converter

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