LEE- Kontrol ve Otomasyon Mühendisliği-Yüksek Lisans
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ÖgeAn online adjustment mechanism for membership functions of single input interval type-2 fuzzy PID controller(Lisansüstü Eğitim Enstitüsü, 2023-05-25) Aldreiei, Oqba ; Güzelkaya, Müjde ; 504191149 ; Control and Automation EngineeringThe characteristics of the footprint of uncertainty (FOU) in interval type-2 membership functions (IT2-MFs) are crucial for the performance and robustness of interval type-2 fuzzy controllers (IT2 FCs). However, existing IT2-FC designs mostly use fixed FOU structures. This study proposes an online adjustment mechanism for membership functions of single input interval type-2 fuzzy PID controller (SIT2-FPID) by adjusting the footprint of uncertainty (FOU) and the weights of the antecedent and consequent membership functions (MFs) respectively to achieve high performance and robustness. The proposed online adjustment mechanism consists of two main parts: relative rate observer (RRO) and adjustment mechanism which has two inputs "error" and "normalized acceleration (Rv)", whereas the "normalized acceleration" provides relative information about the fastness or slowness of the system response. Meta-rules for the modification of the output of online adjustment mechanism (γ) are derived according to the error value and the relative information on the fastness or slowness of the system response and by analyzing the transient phase of the unit step response of the closed-loop system. The output of online adjustment mechanism (γ) in the proposed online tuning method is used as a tuning variable for the footprint of uncertainty (FOU) of the antecedent interval type-2 membership functions and the weights of the consequent crisp membership functions. This provides a dynamic membership functions (MFs) structure, where the heights of the Lower MFs (LMFs) or Upper MFs (UMFs) of each IT2 fuzzy set and the weights of the crisp output are defined as functions of the output of online adjustment mechanism (γ). By doing so, the method accomplishes the task of an online adjustment of the FOU and the weights of the antecedent and consequent membership functions respectively. The single input interval type-2 fuzzy PID controller (SIT2-FPID) with the proposed membership function adjustment mechanism was compared with the conventional PID controller and single input interval type-2 fuzzy PID controller with fixed membership functions through simulations. Throughout the simulation studies seven different performance measures are considered, three of them classical transient system response criteria: settling time (Ts), overshoot (%OS), and rise time (Tr) and the other performance measures are considered as: Integral Absolute Error (IAE), Integral Square Error (ISE), Integral Time Squared Error (ITSE) and Integral Time Absolute Error (ITAE). In addition, a step input and output disturbances have been employed to observe the disturbance rejection performance of the proposed method. The proposed online adjustment mechanism for membership functions method is demonstrated to be effective in linear and non-linear systems through simulations, and to be efficient in compensation of input and output disturbances in a short period of time.
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ÖgeFuzzy logic based clutch torque curve detection algorithm for heavy duty vehicles(Graduate School, 2023-01-24) Cantürk, Ogün ; Üstoğlu, İlker ; 504191124 ; Control and Automation EngineeringIn this thesis, a fuzzy logic-based clutch torque curve learning algorithm is proposed as the second method to eliminate the mentioned disadvantages. The torque curve can be determined with this method without the necessity for any specific maneuver and activation conditions. Using a reference point on the curve, the fuzzy logic-based algorithm determines the position value corresponding to the reference point with respect to different clutch temperatures and the first torque transfer points. In this study, 581 Nm was chosen as the reference point. The fuzzy logic theory was introduced by L. A. Zadeh in 1965. Since then, it has been utilized in numerous fields, including the automotive, transportation, robotics, and chemical industries. The theory basically transforms the relationship between concepts into linguistic rules and permits expert opinions and experiences to be incorporated into system models. Fuzzy controllers consist of three main parts: fuzzifier, rule-based inference engine, and defuzzifier. Mamdani and Takagi-Sugeno type of fuzzy controllers are the most commonly used. MATLAB-Simulink was used for simulation studies. First of all, the conventional algorithm model was developed. The activation conditions, timer, and curve calculation functions used in the model are mentioned in detail. Secondly, two different fuzzy controllers, Takagi-Sugeno and Mamdani types, were designed. The purpose of designing different types of controllers is to compare the performances of the controllers for this problem. While designing the controllers, MATLAB's "Fuzzy Logic Designer" interface was utilized. In order to make a realistic comparison, the same input membership functions and rules are used in the controllers. The inputs of the controllers are selected as the clutch temperature and the first torque transfer point. Three membership functions are defined for each input: "low", "medium" and "high". The output of the controllers is the clutch position corresponding to the reference torque. As with the inputs, three different output membership functions are defined as "low," "medium," and "high" for both controllers. During the design of fuzzy controllers, the relationship between inputs and outputs was determined by analyzing data collected from multiple vehicles. After designing both controllers, a mechanism was created to choose between the conventional algorithm and the fuzzy-based algorithm. The decision mechanism basically compares the reference clutch position values obtained from the two strategies. If the difference between the calculated reference values exceeds a predetermined upper threshold, the error is detected, and the curve obtained from the fuzzy-based strategy becomes equal to the final output. If the difference between the calculated reference values is below a lower threshold, the error is deactivated, and the curve obtained from the conventional algorithm becomes equal to the final output. Thus, as the traditional algorithm will not be activated until the first launch maneuver, the error value will be high and the fuzzy-based strategy will be effective. So, the mechanism eliminates the feeling of poor performance on the first launch. Moreover, the output of the fuzzy controller will be continuously updated based on the change in clutch temperature and the first torque transfer point while driving. The fuzzy controller will be activated if an error is detected, preventing incorrect torque curve learning situations. For testing and validating the developed model, a two-step test procedure was created. First, launch maneuver data was collected for three different clutch temperature ranges: low (40-70°C), medium (70-90°C), and high (90-120°C) from a test vehicle with a 28-ton, construction truck variant. The relationship between traditional and fuzzy controller-based algorithms was examined by feeding the vehicle data to the generated MATLAB-Simulink model. This study was carried out separately for models using Takagi-Sugeno and Mamdani type fuzzy controllers. The obtained clutch torque curves were compared for 40, 70, and 100 °C clutch temperatures, one value from each temperature zone. In the second step of the test, the torque curves obtained from the conventional algorithm, Mamdani, and Takagi-Sugeno type fuzzy controllers for different clutch temperatures were validated by performing launch maneuvers on the same test vehicle. For each test, the maneuvers were repeated with the same gear, accelerator pedal, and road conditions. The verification was done by examining the difference between engine and clutch torque during the launch maneuver. A large difference between torque values indicates that the clutch is in the wrong position. For this reason, the difference between the torque values was defined as the error. Three different performance indexes ISE, ITSE and ITAE were used to compare the performance of the strategies analytically. Since the ITSE and ITAE indices are time-dependent, they evaluate launch maneuvers in terms of duration. The test results were analyzed in three sections as low, medium, and high. At low clutch temperatures, both Mamdani and Takagi-Sugeno fuzzy controllers outperform the conventional algorithm. Moreover, Mamdani provides better results according to ISE index, whereas Sugeno outperforms according to ITAE and ITSE indices at low clutch temperatures. The main reason for this is that when a Sugeno-type fuzzy controller is used, the launch times are reduced. For medium clutch temperatures, all three strategies were yielded similar results. As at low temperatures, Mamdani provides better results according to ISE index, whereas Sugeno outperforms according to ITAE index at medium clutch temperatures. According to the ITSE index, the performance of the two strategies is equal. For all three indices, the traditional algorithm has the lowest performance. However, there is no dramatic difference in the results of the three strategies. For high clutch temperatures, Sugeno has the worst performance according to all three indices. The main reason for this is that the Sugeno type fuzzy controller is much more sensitive to high clutch temperatures than the Mamdani type fuzzy controller. In addition, Mamdani type fuzzy controller has the best performance for all three indices. In general, it was observed that fuzzy controllers improved clutch torque curves. On the other hand, fuzzy controllers increased computational load and simulation times. Both types of fuzzy controllers have improved the performance of the first launch maneuvers. Sugeno type fuzzy controller is highly sensitive to changes in high clutch temperatures. Therefore, it showed poor performance at high temperatures. The Mamdani-type fuzzy controller, on the other hand, succeeded in all three test scenarios.
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ÖgeImproved fuzzy logic based edge detection method on clinical images(Graduate School, 2022-01-07) Çelen, Murat Mert ; Üstoğlu, İlker ; 504191145 ; Control and Automation EngineeringSignal processing is the main field combining electrical engineering and mathematics, used to analyze digital and analog signals. Signal processing deals with the storage, compression, filtering and other processing of signals. These signals can be sound signals, image signals, and other signals. Nowadays images are essential thing for many area. Images can be used in space researches, military applications, marine workings, automotive industry, environment, agriculture and medical science. The area where the signal type is processed is that the input is an image, and the output is also an image, which is called image processing. Image Processing is one of the main research area in the disciplines of computer science and engineering. Image processing is a methods which performs operations on an image, on account of get an information from image. The progress of image processing are improved by the help of: the development of technology, the development of discrete theory, the demand for a pretty wide range of applications. It can be divided into digital and analog image processing. Image processing for analog images is used for hard copies of photos. Digital image processing uses computers to process digital images. Image processing has various kind of application such as sharpening, blurring, contrast adjustment, and edge detection etc. Edge detection is helpful for applications in the fields such as fingerprint matching, medical diagnosis, license plate detection, biomedical imaging, pattern recognition and machine vision. Edge detection technique makes the high intensity valued pixels visible. Edge detection is a compelling assignment. When edge detection must be applied to noisy images, it becomes more difficult. The idea of fuzzy logic helps to get rid of this problem with expert knowledge. The concept of fuzzy logic was first proposed in the 1960s by Professor Lütfi Aliasker Zade in Berkeley. Lütfi Aliasker Zade is committed to translating natural language into computer language, but it is not easy to translate into computer language terms 0 and 1. Zade proposed a shape of polyvalent logic within which the truth valuation of variables is also any real number between 0 and 1 whereas classical logic theory is utilizing with values false or true. Fuzzy logic can be summarized as predicated on the observation that individuals make decisions supported vague and non-numerical information. Fuzzy models are numerical implies of speaking to dubiousness and uncertain data. These models have the inclination of deciphering and controlling information and information that are non-certain. Additionally, it's conceivable to characterize linguistic variables like brief, exceptionally brief, long, or exceptionally long with fuzzy logic. Lütfi Zade's proposed theory fuzzy logic has been applied to various fields such as robotics, artificial intelligence, modeling and controlling system which is nonlinear or digital image processing. These fields used type-1 fuzzy logic until Prof. Lütfi A. Zade presented type-2 fuzzy logic in 1975. Fuzzy logic's type-2 theory was improved for uncertainties and non-linearity due on type-1 fuzzy rules, it shows fuzzy logic frameworks on type-2 are more fruitful than fuzzy logic frameworks on type-1 to unravel vulnerabilities. Be that as it may, working with fuzzy logic frameworks on type-2 are distant more advanced than working with fuzzy logic frameworks on type-1. In this thesis we will talk about a type-1 edge detection with fuzzy logic implementation for medical brain images, with the assistance of digital image, and digital image processing. This thesis gives you the performance comparison of widely used edge detection methods and improved edge detection with fuzzy logic method with interpreting digital images with the help of image enhancement and restoration and performing operations on images such as blurring, contrast adjustment. Different sources of digital images will be tested and results for each source will be provided.
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ÖgeZaman gecikmeli sistemler için kural kaydırma tabanlı bulanık mantık kontrolör tasarımı(Lisansüstü Eğitim Enstitüsü, 2022-02-01) Ateşova, Müge ; Güzelkaya, Müjde ; 504171136 ; Kontrol ve Otomasyon Mühendisliği ; Control and Automation EngineeringZaman gecikmeli sistemlerin kontrolü pratikte en çok karşılaşılan kontrol problemlerinden biridir. Literatürde bu kontrol problemi üzerine pek çok çalışma ve uygulama bulunmaktadır. Zaman gecikmeli sistemlerde karşılaşılan sorunların temeli sistemden gözlenen bilginin geçmişe ait olmasına dayanmaktadır. Bu durumun kontrolör tarafından algılanması mümkün olmadığı için başarısız sonuçlara neden olabilmektedir. Probleme temel bir bakış açısıyla yaklaşmak gerekirse, kontrol sistemine giren bilginin geçmiş zamana ait olması durumunda bunun algılanıp duruma göre bir ayarlama yapılmasının soruna çözüm olması beklenir. Bulanık mantık kontrol yapıları üzerine yapılan çalışmalardan bazıları kontrolörün katsayılarını değiştirmeden kural tabanının kaydırılması ile zaman gecikmesinin sistem yanıtı üzerindeki olumsuz etkilerinin azaltılabileceğini göstermiştir. Sistem modelleri elde edilirken sahip olabilecekleri zaman gecikmesinin dikkate alınmış olması gerekir. Ancak zaman gecikmesinin gerçekte modelde bulunan değerinden farklı olduğu durumlar ile karşılaşılabilir. Bu durumda kontrol sisteminden beklenilen başarım elde edilemez. Bu çalışmada, ölü zamanın modelde bulunan değerinden daha az veya daha fazla olduğu durumlar için modele göre belirlenmiş bulanık mantık PID kontrolörünün kural tablosu değiştirilmiştir. Bu işlem sırasında bulanık kontrolör kural tablosu satırları uygun miktar ve yönlerde kaydırılmıştır. Kural tablosunun düzenlenmesinin etkisini görebilmek adına çalışmalar boyunca her bir sistem modeli için bulanık mantık kontrol katsayıları genetik arama algoritması yardımıyla belirlenmiştir. Genetik arama algoritması için arama kriteri zaman ağırlıklı hata karelerinin toplamı (ITSE) olarak seçilmiştir. ITSE kriteri aynı zamanda sistemin farklı kural tabanları ile başarımını incelemek için de kullanılmıştır. Ayrıca, sistemdeki zaman gecikmesinin değişmesi durumuna kontrol yönteminin bu değişime bağlı olarak uygun kural tabanını kullanabilmesi için öz-ayarlamalı kural tabanı yöntemi önerilmiştir. Bu amaçla sistem modelinde var olan zaman gecikmesinin çeşitli değişimleri için uygun olan kural tabanları belirlenmiştir. Bu kural tabanları arasında, belirlenen zaman gecilmesine bağlı olarak geçiş yapabilen bir kontrol yapısı kurulmuştur. Öz ayarlamalı kontrol yapısı, kural tabanı kaydırılmamış bulanık mantık kontrol yöntemi ve zaman gecikmesi bilinen sistemler için belirlenmiş olan kural tabanı kaydırılmış bulanık mantık kontrol yöntemi ile karşılaştırılmıştır. Elde edilen ITSE değerleri tablolar halinde verilirken, sistem yanıtları grafik halinde gösterilmiştir. Tahmin edilebileceği gibi zaman gecikmesi bilinen sistemler için uygun kural tabanı kaydırması ile elde edilen kontrol sistemlerinin benzetim sonuçları öz-ayarlamalı kontrol yönteminin uygulandığı zaman gecikmesi bilinmeyen sistemlerin benzetim sonuçlarından belirlenen başarım kriterine göre daha başarılı olmuştur. Fakat, çizelge ve grafikler göstermektedir ki öz-ayarlamalı kontrol yöntemi ile kural tabanı kaydırılmamış bulanık mantık kontrol yöntemini kıyaslandığında öz-ayarlamalı kural tabanı yapısı daha başarılı olmuştur.