LEE- Kontrol ve Otomasyon Mühendisliği-Yüksek Lisans

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  • Öge
    Functional safety for heavy-duty transmissions
    (Graduate School, 2022-09-22) Bozdağ, Konuralp Tevfik ; Üstoğlu, İlker ; 504191117 ; Control and Automation Engineering
    The number of electrical and electronic equipment and software used in vehicles is increasing day by day. Apart from passive safety precautions for these hardware and software used, now active precautions are also taken. While the seat belt was a passive safety measure in the first years when vehicles became a necessity for the society, many software and hardware measures are now taken for the safety of the driver and passengers. Thanks to the autonomous transportation, the driver's place in transportation is decreasing, while his safety and security are gaining more and more importance. A system or piece of hardware must operate appropriately in accordance with the system's inputs in order for functional safety to be a component of the overall safety framework. To put it another way, functional safety refers to the capacity to recognize potentially dangerous circumstances and to trigger a protective or corrective device or mechanism to stop the development of hazardous events or to lessen their potential effects. The only means for the driver to act in an emergency while driving an automobile is to press the brake pedal. However, thanks to the software created thanks to functional safety, accidents can be prevented by intervening to the vehicle faster than the driver in an unexpected situation. Electrical, electronic, and programmable electronic everything is determined within the framework of certain rules and steps, functional safety analyzes and safety levels within the purpose of the IEC 61508 standard. Functional safety is included in the ISO 26262 standard group, being customized for the automotive sector. The ISO 26262 standard series describe how software and hardware for an automotive should be developed in certain road conditions and accident situations. In this thesis, the subject of functional safety will be examined in the automatic transmission system used in heavy vehicles. Today, due to globalization and the increase in consumer needs, logistics and transportation sectors gain more importance. Transportation is of great importance in these sectors and heavy vehicles have a large share in this sector. Trucks, trucks, etc., for both the safety of people and the transportation of products without any damage. It is important that vehicles are safe and secure. Among the working subjects of the automotive industry, the transmission software and designs of heavy-duty vehicles have an important value. The heavy-duty transmission to be analyzed is an automated manual transmission, with 16 forward and 4 reverse gears. Gear shifts are not only with synchromesh, but also by using 3-stage actuators, more combinations are created with less gears, and a lighter transmission is designed than expected. However, functional safety becomes more important in heavy-duty vehicles that have more hardware and software in terms of software and hardware. Because, in heavy-duty vehicles, the gearbox not only provides regular and desired torque transmission, but also fulfills different duties depending on the service type. Therefore, safety analyzes and created scenarios are investigated in more detail. In this research, firstly, the safety analysis of the heavy-duty vehicle was carried out according to the ISO 26262 standard group. In order to make a more detailed examination as the system where the safety analysis will be made, the system limits have been determined as automatic transmission and actuators. Then, the problems that may arise in the vehicle and transmission are considered and it is determined what kind of dangers may occur. Considering these hazards, hazard and risk analysis has been made for specific scenarios. What kind of safety goals should be taken against the hazards that may arise because of the hazard and risk analysis and how long it should take are defined in the functional safety concept. The analysis made was examined in detail and the safety requirements were established for the transmission software. The safety targets and requirements that emerged as a result of the safety analyzes were tested in the simulation environment. By means of model-based software, a dynamic model of the heavy-duty vehicle is created, and simple transmission algorithms are demonstrated. By creating virtual hazards and scenarios in Matlab & Simulink environment, vehicle models with and without functional safety software are compared.
  • Öge
    Robotic fish for monitoring water pollution
    (Graduate School, 2022-02-01) Ansari, Mohammed Javed ; Doğan, Mustafa ; 504161131 ; Control and Automation Engineering
    The vast majority of the earth's surface is covered by water. Some parts of the ocean are so deep that even Mount Everest would be lost into them as if it never existed. Water bodies, irrespective of fresh or salty, big or small, all of them host some of the most unique ecosystems. Mankind is known to have set its sails into the oceans for time immemorial now. But it has only been possible in recent years that they have dived inside by the means of HOVs, ROVs, and AUVs. And still, most of it remains unexplored. Every living thing from a unicellular amoeba to Antarctic blue whales including every single plant needs water to survive. Otherwise, the earth would be as barren as any other planet known so far. The key to fact that life exists on the earth is water. But unfortunately, the amount of garbage of all kinds being dumped into the sources of water pollutes them and in a long run adversely affects and endangers the living things on planet earth. As our very existence depends on water, it's indispensable to monitor and take essential steps to preserve the water quality accordingly. Not only does water avail a sustainable condition for the terrestrial inhabitants, but also is a habitat to a huge number of species within. One of the most well-known species among these aquatic animals is fish. In this work, a brief study of types of fishes along with their structural definition is carried out to determine how they propel and swim in the water with their fin and then eventually use the discoveries to biomimetically design and implement a robotic fish capable of exploring water and taking certain readings with inbuilt sensors. The thus obtained readings can be used to monitor water. The robotic fish here tries moving in the water replicating the motion behaviors of a fish. This study consists of 5 different parts. Chapter 1 provides a brief introduction of the whole idea and the classification of fish according to their swimming behavior. Fishes swim in the water using their fins. They use their fins to produce a propulsive force that pushes them forward. Depending upon which part of the fish and how it pulsates fishes can be categorized into different classes. These classifications help study fishes better. A detailed categorization on the basis of various grounds is further discussed in this chapter. A common approach to classify fishes is based on the modes of propulsion that a fish applies while swimming i.e. whether undulatory or oscillatory methods of generating propulsive forces. These two categories of fish swimming modes are BCF (body and/or caudal fin) locomotion, and MPF (median and/or paired fin) locomotion. A thing common in these modes of propulsion is that the caudal fins play the most important role in producing the propulsive force generation. In this study, a "Carangiform & Fusiform" model has been adapted for replication. The first chapter also gives a brief description of "Biomimetics" along with some of its popular applications in various fields. Later in this chapter, the overall implementation of this work has been mentioned. Chapter 2 discusses works of a similar kind. It also comprises the methods used in other similar works. The caudal fin drive mechanism can be of single, multiple, or compliant type. It is already known that the caudal fin plays the most important role in swimming and maneuvering. And the stiffness of the joint that connects the caudal fin to the body of the fish is equally important for efficient swimming. Unlike other similar works, Turfi uses a single joint method with a soft caudal fin. The outer cover of Turfi was designed using SolidWorks. The 3D model was later printed using a 3D printer. The outer body of Turfi was divided into 2 halves while designing. The first half enclosed all the electronics (including the SD card module, battery, sensors, processor, and driver circuits) and the motors. The pectoral fins are controlled using micro servo motors that help Turfi in maneuvering and the caudal fin is driven using a dc motor attached to a reduction mechanism. The other half of Turfi is the caudal tail and its mechanism that creates the oscillatory motion in the caudal fin by the means of the dc motor. The caudal fin drive mechanism converts the rotary motion of the dc motor to oscillatory motion. The front enclosure part was 3D printed using Polylactic Acid (PLA) because of its stiffness. The posterior i.e., the caudal fin was made using Thermoplastic Polyurethane (TPU). TPU is best known for flexibility. Making the caudal fin with TPU gives the caudal fin a soft and flexible structure thus making the propulsion wavy and smooth. The ESP32 used as the processor is also embedded with a WiFi module. ESP32 is programmed to create an Async WiFi server. The asynchronous server allows Turfi to take the readings and store them on an SD card even when offline. And when connected can deliver all the data collected at once. This helps Turfi to navigate and collect data irrespective of its connection to the base station. Turfi while navigating underwater takes the sensor readings and stores them into an SD card. After the completion of navigation, Turfi resurfaces and connects with the base station using WiFi and sends all the readings made during the navigation. Turfi later. These readings can be accessed using an IP provided by ESP32. These details are discussed in Chapter 3. As this study progressed further it was seen that Turfi can be programmed in various ways to accomplish different tasks. In the 4th Chapter, the results of two different tests are included. In the first test, Turfi was programmed to take readings at a certain depth (i.e., 20cm). A PID controller using PID Library by Brett Beauregard was used to track the depth based on the readings from the depth sensor. The second test was similar to the first one except that Turfi was instructed to take left and right turns. 5th Chapter concludes this work by describing the complexity of multi-fin locomotion underwater. It also briefly explains how Turfi can be developed in order to accomplish further. Upgrades such as a camera to record underwater, sensors to measure pH, oxygen level, salinity, etc. can be attached to Turfi. These sensors can help Turfi monitor underwater in a more detailed way. An exit mechanism is also proposed in this section. The exit mechanism would help Turfi resurface in case the battery is below a certain level or once the navigation is complete. Once atop, the whereabouts of Turfi can be known using GPS. There have been works of similar nature done priorly. But most of them tend to focus on a descriptive analysis of the swimming behavior of a fish and then replicating it. In this work, the scope has been slightly widened by adding the sensors to make required readings. One major hindrance similar to the ones of previous works i.e., limitation to wirelessly communicate well is experienced while working on this project as well. Thus, a different approach is applied in this study. In this approach, Turfi is instructed to follow a certain navigation route. While navigating underwater, Turfi also stores the sensor readings on an SD card. These data can be retrieved wirelessly from Turfi over WiFi. Thus, obtained data can be used for further processing.
  • Öge
    An 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 Engineering
    The 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.
  • Öge
    Zaman 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 Engineering
    Zaman 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.