Yumuşak hesaplama yöntemlerinin geliştirilmesi ve elektriksel sürücülerdeki uygulamaları
The improvement of soft computing methods and its applications in electric drives
Yazar: MEHMET KARAKÖSE
Danışman: DOÇ.DR. ERHAN AKIN
Fırat Üniversitesi / Fen Bilimleri Enstitüsü / Elektrik-Elektronik Mühendisliği Ana Bilim Dalı
Derece: Doktora, 2005
Konu: Elektrik ve Elektronik Mühendisliği = Electrical and Electronics Engineering

Özet:
ÖZET Doktora Tezi YUMUŞAK HESAPLAMA YÖNTEMLERİNİN GELİŞTİRİLMESİ VE ELEKTRİKSEL SÜRÜCÜLERDEKİ UYGULAMALARI Mehmet KARAKÖSE Fırat Üniversitesi Fen Bilimleri Enstitüsü Elektrik-Elektronik Mühendisliği Anabilim Dalı 2005, Sayfa: 205 Birçok metodolojiden oluşan yumuşak hesaplamanın gelişmesi son on yılı aşkındır önemli araştırma ilgisi çekmiştir. Bu metodolojilerin her biri matematiksel olarak modellenemeyen veya modellenmesi oldukça zor olan gerçek dünya problemleri için çözümler sağlama ve modelleme yeteneğine sahiptir. Bulanık mantık, yapay sinir ağlan, genetik algoritmalar, olasılıksal çıkarım, makina öğrenmesi, kaos teorisi, destek vektör makinalar, veri madenciliği, yapay bağışık sistemler ve kaba kümeler yumuşak hesaplama kavramı içerisinde verilebilir. Yumuşak hesaplama kontrol, işaret işleme, sınıflandırma, karar verme, örüntü tanıma, robotik ve sistem modelleme gibi alanlarda önemli bir uygulama potansiyeline sahip olmasına rağmen bazı dezavantajlara sahiptir. Bu tezin amacı yumuşak hesaplama teorisini araştırmak, onun bileşenlerinde yeni yaklaşımlar önermek ve yumuşak hesaplama metodolojilerini elektriksel sürücülerde bazı problemleri çözmek için uygulamaktır. Tezde yumuşak hesaplama yöntemlerinde dört yeni yaklaşım ve üç elektriksel sürücü uygulaması çalışılmıştır. Teorik çalışmalarda ilk olarak sağlam, verimli ve karmaşıklığı az denetleyiciler tasarlamak amacıyla kontrol işlevini yerine getirmek için dokuz kurallı bulanık denetleyicilerden oluşan ve blok tabanlı bulanık denetleyiciler olarak adlandırılan yeni bir bulanık sistem yaklaşımı önerilmiştir. İkinci olarak bulanık mantık sistemler için sinüs modülasyonlu, parabolik ve ters parabolik olmak üzere üç yeni üyelik fonksiyonu önerilmiştir. Üçüncü olarak tip-1 ve tip-2 bulanık kümeleri kullanan bulanık çok kriterli karar verme yöntemlerinde yeni geliştirmeler çalışılmıştır. Önerilen bu yöntemlerin temel özellikleri: geleneksel bulanık integralin bulanık yoğunluk değerleri ile uyarlanma kabiliyeti alması ve XVItype-2 fuzzy sets improves the flexibility of the control systems. Lastly, a type-2 fuzzy based activation function for multilayer feedforward neural networks was proposed. Instead of other activation functions, the proposed approach uses a type-2 fuzzy set to accelerate backpropagation learning. In electrical drive application section, first study includes a fuzzy filter that is heuristic- knowledge-based algorithm to detect and discard feedback signal error and noise. For this purpose, a fuzzy filter is designed and tested on hysteresis controlled induction motor currents. Second study based on determining output of compensated voltage model with fuzzy algorithm. The proposed algorithm can be used to accurately measure the sensorless vector controlled induction motor flux including its magnitude and phase angle. Final study proposes a fuzzy logic based smooth transition method between flux models for low speed operation of a stator flux oriented induction motor drive. The major task of this transition algorithm is to eliminate jerks on the torque during the transition between the flux estimation models. Briefly, the theory and electrical drives applications of soft computing methodologies were studied in this thesis. Keywords: Soft computing, intelligent systems, hybrid soft computing, electrical drives, sensorless vector control. XIX

Abstract:
ABSTRACT PhD Thesis THE IMPROVEMENT OF SOFT COMPUTING METHODS AND ITS APPLICATIONS IN ELECTRICAL DRIVES Mehmet KARAKÖSE Firat University Graduate School of Natural and Applied Sciences Department of Electrical and Electronics Engineering 2005, Page: 205 The development of soft computing that consist of many methodologies has attracted considerable research interest over the past decade. Each of these methodologies is capable of model and enables solutions to real world problems which can not be modeled or too difficult to model, mathematically. Fuzzy logic, artificial neural networks, genetic algorithms, probabilistic reasoning, machine learning, chaos theory, support vector machines, data mining, artificial immune systems and rough sets can be given in the soft computing concept. Although soft computing has an important application potential in areas such as control, signal processing, classification, decision making, pattern recognition, robotics and system modeling, it has some common drawbacks. The aim of this thesis is to investigate the soft computing theory, propose new approaches in its components and apply to solve some problems in electrical drives with soft computing methodologies. In this thesis, four new approaches and three electrical drive applications in soft computing methods were studied. In theoretical studies, firstly, a novel fuzzy system approach called block based fuzzy controllers that is constructed by several fuzzy controllers with nine fuzzy rules to carry out control tasks was proposed to design robust, efficiency and low complexity controllers. Secondly, new three membership functions as sinus modulated, parabolic and inverse parabolic for fuzzy logic systems were proposed. Thirdly, new improvements in fuzzy multicriteria decision-making methods using type-1 and type-2 fuzzy sets were used. Main features of these proposed methodologies are the conventional fuzzy integral gets adaptation capability with fuzzy density values, and a decision making method with a combination of fuzzy integral and XVIIItype-2 fuzzy sets improves the flexibility of the control systems. Lastly, a type-2 fuzzy based activation function for multilayer feedforward neural networks was proposed. Instead of other activation functions, the proposed approach uses a type-2 fuzzy set to accelerate backpropagation learning. In electrical drive application section, first study includes a fuzzy filter that is heuristic- knowledge-based algorithm to detect and discard feedback signal error and noise. For this purpose, a fuzzy filter is designed and tested on hysteresis controlled induction motor currents. Second study based on determining output of compensated voltage model with fuzzy algorithm. The proposed algorithm can be used to accurately measure the sensorless vector controlled induction motor flux including its magnitude and phase angle. Final study proposes a fuzzy logic based smooth transition method between flux models for low speed operation of a stator flux oriented induction motor drive. The major task of this transition algorithm is to eliminate jerks on the torque during the transition between the flux estimation models. Briefly, the theory and electrical drives applications of soft computing methodologies were studied in this thesis. Keywords: Soft computing, intelligent systems, hybrid soft computing, electrical drives, sensorless vector control. XIX