Cetin Elmas

A neuro-fuzzy controller for speed control of a permanent magnet synchronous motor drive

This paper introduces a neuro-fuzzy controller (NFC) for the speed control of a PMSM. A four layer neural network (NN) is used to adjust input and output parameters of membership functions in a fuzzy logic controller (FLC). The back propagation learning algorithm is used for training this network. The performance of the proposed controller is verified by both simulations and experiments. The hardware implementation of the controllers is made using a TMS320F240 DSP. The results are compared with the results obtain from a Proportional+Integral (PI) controller. Simulation and experimental results indicate that the proposed NFC is reliable and effective for the speed control of the PMSM over a wide range of operations of the PMSM drive.

Adaptive fuzzy logic controller for DC-DC converters

This paper introduces a complete design method to construct an adaptive fuzzy logic controller (AFLC) for DC-DC converter. In a conventional fuzzy logic controller (FLC), knowledge on the system supplied by an expert is required for developing membership functions (parameters) and control rules. The proposed AFLC, on the other hand, do not required expert for making parameters and control rules. Instead, parameters and rules are generated using a model data file, which contains summary of input-output pairs. The FLC use Mamdani type fuzzy logic controllers for the defuzzification strategy and inference operators. The proposed controller is designed and verified by digital computer simulation and then implemented for buck, boost and buck-boost converters by using an 8-bit microcontroller.

Genetic Algorithm Based On-line Tuning of a PI Controller for a Switched Reluctance Motor Drive

Abstract A switched reluctance motor (SRM) is suitable for many variable-speed and servo-type applications, and is getting increasing attention in the motor drive industry. However, the SRM has highly nonlinear characteristics since the developed torque of the SRM is a nonlinear function of both phase current and rotor position, and the SRM always operates with magnetic saturation to maximize torque/mass ratio. These nonlinearities of the SRM drives make the conventional PI (proportional + integral) controller a poor choice for application where high dynamic performance is desired under all motor operating conditions. The genetic algorithm (GA) can give robust adaptive response of a drive with nonlinearity, parameter variation and load disturbance effect. In this article, the genetic PI speed controller was applied to the speed loop of the SRM by replacing the conventional PI speed controller. The genetic PI controller software was implemented using C+ + Builder on a PC. Both the conventional and the genetic PI controller for the SRM are implemented by using a TMS320F240 digital signal processor. The results show that the genetic PI controller is less sensitive to the parameter variations and disturbances.

A data fusion framework with novel hybrid algorithm for multi-agent Decision Support System for Forest Fire

In this study Forest Fire Decision Support System (FOFDESS) which is a multi-agent Decision Support System for Forest Fire has been presented. Depending on the existing meteorological state and environmental observations, FOFDESS does the fire danger rating by predicting the forest fire and it can also approximate fire spread speed and quickly detect a started fire. Some data fusion algorithms such as Artificial Neural Network (ANN), Naive Bayes Classifier (NBC), Fuzzy Switching (FS) and image processing have been used for these operations in FOFDESS. These algorithms have been brought together by a designed data fusion framework and a novel hybrid algorithm called NABNEF (Naive Bayes Aided Neural-Fuzzy Algorithm) has been improved for fire danger rating in FOFDESS. In this state, FOFDESS is an integrated system which includes the dimensions of prediction, detection and management. As a result of the experiments, it was found out that FOFDESS helped determining the most accurate strategy for fire fighting by producing effective results.

NEFCLASS-based neuro fuzzy controller for SRM drive

Switched reluctance motor (SRM) is increasingly employed in industrial applications where variable speed is required because of their simple construction, ease of maintenance, low cost and high efficiency. However, the SRM performance often degrades for the machine parameter variations. The SRM converter is difficult to control due to its nonlinearities and parameter uncertainties. In this paper, to overcome this problem, a neuro fuzzy controller (NFC) is proposed. Heuristic rules are derived with the membership functions of the fuzzy variables tuned by a neural network (NN). The algorithm is implemented on a digital signal processor (TMS320F240) allowing great flexibility for various real time applications. Experimental results demonstrate the effectiveness of the NFC with various working conditions of the SRM.

An educational tool for power electronics circuits

In this study, an educational tool has been prepared for a shorter term and more economic education of power electronics circuits. In parallel with the improvements of semiconductor technology, the development of power electronics circuits has magnified the importance of either teorical or practical education of power electronics course. The education of power electronic circuits in laboratory is an agelong, costly piece of work. In this study, to overcome the mentioned negativities, a tool has been prepared for the education of power electronic circuits. The tool, which has been prepared on C++ Builder environment has a flexible structure and a graphical interface. It has enabled the analysis of working principles of the circuits and traceability of the system response by the help of graphics, under different conditions created by changing the values of circuit elements.

An Educational Tool for Fuzzy Logic Controller and Classical Controllers

In this study, an educational tool has been presented to teach fuzzy logic controller (FLC) and classical controllers to undergraduate and graduate level students, so that students could establish a thorough understanding and be able to compare FLC with classical controllers. The tool has flexible structure and user‐friendly graphical interface. Direct current (DC) motor speed control has been presented to help students learn and compare the FLC and classical controllers. Students practice on both controller, interpret and draw conclusions related to system parameters by changing parameters of FLC and classical controller with various working conditions.

Fuzzy control of dc–dc converters based on user friendly design

The aim of this paper is to show how to build a fuzzy controller and its membership functions automatically. In a fuzzy logic controller (FLC), the proposed method allows one easily to construct a set of membership functions, called shrinking-span membership functions (SSMFs). The FLC uses Mamdani-type fuzzy controllers for the defuzzification strategy and inference operators. The FLC hardware implementation is performed on an 8-bit microcontroller. Simulation results and experimental results demonstrate that the converter can be regulated with good performance even when subjected to input disturbance and load variation. The presented approach is generally valid for the design of an FLC, and can be applied to any dc–dc converter topologies.

PC Based Educational Tool for a Switched Reluctance Drive with Fuzzy Logic

This paper introduces a PC based educational tool for a switched reluctance drive (SRD) with fuzzy logic which is prepared for undergraduate and graduate level students. The paper first describes how the switched reluctance drive system works. Then details are given of how to estimate the flux linkage by using fuzzy set theory. Finally an application of fuzzy logic control to an SRM drive is presented.

Fuzzy diffusion filter with extended neighborhood

Anisotropic diffusion filters, which are motivated from heat diffusion between mediums, have become a widely used technique in the field of image processing. In the initial proposals of anisotropic diffusion filters, 4-neighborhood values with diffusivity functions are computed independently for each spatial location because of numerical approximation. However, anisotropic diffusion filters could not be used in real-time image and video processing applications because they need diffusivity parameters, which must be specified by users in every sampling period. In this study, a fuzzy adaptive diffusion filter using extended neighborhood without diffusivity functions has been developed. The fuzzy adaptive diffusion filter does not require any parameter chosen by user and therefore they could be employed in real-time applications. In the fuzzy adaptive diffusion filter, a similarity transformation by means of relation matrix and fuzzy logic is carried out. Accordingly, the similarity image, output of transformation, is directly used as a heat diffusion coefficient in the diffusion filter. Results show that the fuzzy adaptive diffusion filter is very efficient for removing noise in image while preserving edges.