Bulanık kontrol ve uygulamaları
Fuzzy control and applications
- Tez No: 83118
- Danışmanlar: DOÇ. DR. METİN GÖKAŞAN
- Tez Türü: Yüksek Lisans
- Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 1999
- Dil: Türkçe
- Üniversite: İstanbul Teknik Üniversitesi
- Enstitü: Fen Bilimleri Enstitüsü
- Ana Bilim Dalı: Elektrik Mühendisliği Ana Bilim Dalı
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 79
Özet
ÖZET Bu çalışma son yıllarda çok hızlı bir gelişme gösteren bulanık sistemler ve bulanık kontrol konusundaki gelişmeleri tamtmaktadır. Gerçek hayatta karşımıza çıkan kontrol problemlerinin birçoğu en iyi dilsel tanımlamalarla açıklanabilir ve ayrıca kontrolünde çok hassas olunması gerekmez. Bu nedenle bulanık kontrolün en iyi uygulama alanları lineer olmayan, iyi tanımlanmamış, zamanla değişen sistemlerdir. Sistem karmaşıklaştıkça bulanık kontrol klasik kontrol karşısında ekonomik bir alternatif olmaya başlamaktadır. Aslında günlük hayatımızın hemen her arımda farkında olmadan bulanık kontrol uygulanmaktadır. Bir çok problem çeşitli dilsel ifadeler kullanarak farkında olmadan çok iyi bir şekilde çözülebilmektedir. Örneğin yıllardır araba kullanan bir insan bir arabanın nasıl parkedileceğini dilsel değişkenlerle ifade ederek çok doğru bir şekilde açıklayabilmektedir. Bulanık kontrolün de amacı; bu uzman bilgisini ve tecrübesini çeşitli dilsel değişkenlerle formüle ederek süreç kontrolünde kullanmaktır. vm
Özet (Çeviri)
FUZZY CONTROL and APPLICATIONS SUMMARY The field of fuzzy systems and control has been making rapid progress in recent years. With the practical success of fuzzy control in consumer products and industrial process control, there has been an increasing work on theoretical studies of fuzzy systems and fuzzy control. As a result of these studies, fuzzy systems and fuzzy control is becoming clearer. In literature, there are two kinds of justification for fuzzy systems theory. 1. The real world is too complicated and obtaining mathematical model is difficult or impossible. Therefore approximation must be done to obtain a reasonable model. 2. In the complicated systems, human knowledge becomes increasingly important. Then, there must be a theory to formulate human knowledge systematically, to use together with other information like mathematical models and sensory measurements. What Are Fuzzy Systems? Fuzzy systems are knowledge-based or rule-based systems. The IF-THEN rules are some words to introduce the fuzzy system and these rules gives the fuzzy membership functions. For example, the following is a fuzzy IF-THEN rule. IF the speed of a car is high, THEN apply less force to the accelerator A fuzzy system is constructed from a collections of fuzzy IF-THEN rules. In order to use fuzzy systems in engineering systems, a simple method is to add a fuzzifier, which transforms a real valued variable into a fuzzy set and a defuzzifier, which transforms a fuzzy set into a real valued variable, to the output (Figure 1). Figure 1. Basic Configuration of Fuzzy Controller IXWhere Are Fuzzy Systems Used and How? Fuzzy systems have been applied to a wide variety of fields ranging from control, signal processing, communications, integrated circuit manufacturing and expert systems to business, medicine, psychology, etc. However the most significant applications have concentrated on control problems. Fuzzy systems can be used as open-loop controllers or closed-loop controllers. When it is used as an open-loop controller, the fuzzy system usually sets up some control parameters and then the system operates according to these control parameters. When it is used as a closed-loop controller, the fuzzy system measures the outputs of the process and takes control actions on the process continuously. Applications of fuzzy systems in industrial processes belong to this category. Fuzzy Logic Controller (FLC) : FLC is composed of four components as shown in Figure 1, which is fuzzification interface, a knowledge or rule base, fuzzy inference engine, and a defuzzification interface. The fuzzification interface involves the following functions: 1. Measures the real values of the input variables. 2. Performs a scale mapping that transfers the range of values of input variables into corresponding universes of discourse. 3. Performs the function of fuzzification that converts input data into suitable linguistic values which may be viewed as labels of fuzzy set. The knowledge base comprises a knowledge of the application domain and the control goals. It consists of a data base and a linguistic(fuzzy) control rule base. 1. The data base provides necessary definitions which are used to define linguistic control rules and fuzzy data manipulation in a FLC. 2. The rule base characterizes the control goals and control policy of the domain experts by means of a set of linguistic variables. The fuzzy inference engine is the kernel of an FLC it has capability of simulating human decision making based on fuzzy concepts and the rules of inference in fuzzy logic. The defuzzification interface performs the following functions. 1. A scale mapping, which converts the range of values of output variables into corresponding universes of discourse.2. Defuzzification which yields a nonfuzzy control action from an inferred fuzzy control action. Fuzzy Controller Design Conventional controller design starts with a mathematical model of the process, on the other hand fuzzy controller design starts with heuristics and human experience which is defined in terms of fuzzy IF-THEN rules. Experienced human experts can provide heuristics and rule -of-thumb that are very useful for controlling the process. The design approaches for fuzzy controllers are classified into two categories. The first one is trial-and- error approach, the other one is the theoretical approach. fuzzy control conventional control heuristics and human expertise mathematical model nonlinear controller 13 nonlinear control theory Figure 2. Fuzzy Control Versus Conventional Control In the trial-and-error approach, a set of fuzzy IF-THEN rules collected from human experts, operating manuals or using experience based knowledge. Then fuzzy controllers are designed from these fuzzy IF-THEN rules, finally, the fuzzy controllers are tested in the real system and the performance of the system is not satisfactory,- the rules are fine-tuned or rules are redesigned. In the theoretical approach the structure and parameters of the fuzzy controller are designed in such a way that certain performance criteria are guaranteed. Design of Fuzzy Systems from Input-Output Data Fuzzy Systems are used to formulate human knowledge. Expert human knowledge about a particular engineering problem may be classified as conscious knowledge and subconscious knowledge. By conscious knowledge, fuzzy IF-THEN rule can be constructed easily. For subconscious knowledge, some demonstrations are required for the typical situations of the system. When the expert is demonstrating, the inputs and outputs are measured. These input-output pairs are are a set. The fundamental problem is to construct fuzzy systems using these input-output pairs. XIExpert Knowledge Conscious Knowledge Fuzzy IF-THEN Rule Subconscious Knowledge Expert Demonstrations Measure the Demonstration Input and Output Values Input-Output Pairs Fuzzy Systems Figure 3. Converting expert knowledge into fuzzy systems Fuzzy Controller Types So far, mainly two types of fuzzy logic controller studied : one position-type fuzzy controller which generates control input(u) from error(e) and error rate (e), and the other is velocity-type fuzzy logic controller which generates incremental input(Au) from error and error rate. The former is called PD FLC and the latter is called PI FLC according to the information they process. Using PD FLC and PI FLC, PID FLC controller was developed. For complex systems the single loop control systems may not effectively achieve the control objectives, and hybrid controllers are used in this situation. Figure 4. Fuzzy PD Controller xuFigure 5. Fuzzy PI Controller In the literature, some hybrid fuzzy controllers applications can be seen. The main advantage of two level control is that different controllers can be designed to different objectives, so that each controller is simpler and performance is improved. First Level Figure 6. a Architecture of a two-level fuzzy control system Figure 6.b Architecture of a two-level fuzzy control system Conclusion In this thesis, fuzzy logic and fuzzy controllers are investigated and some controller simulations are realized. xm
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