LEE- Kontrol ve Otomasyon Mühendisliği-Doktora

Bu koleksiyon için kalıcı URI

http://hdl.handle.net/11527/19361

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Konu "Bulanık denetim" ile LEE- Kontrol ve Otomasyon Mühendisliği-Doktora'a göz atma
  • Öge
    Analysis and design of general type-2 fuzzy logic controllers
    (Fen Bilimleri Enstitüsü, 2020) Sakallı, Ahmet ; Kumbasar, Tufan ; 656903 ; Kontrol ve Otomasyon Mühendisliği Bilim Dalı
    This thesis presents new interpretations on the design parameters of the general type-2 fuzzy logic controllers by investigating their internal structures, proposes novel systematic design approaches for the general type-2 fuzzy logic controllers based on comprehensive and comparative analyses, and validates theoretical findings as well as proposed tuning methods via simulation and real-time experiments. The fuzzy systems have been successfully realized in a wide variety of engineering areas such as controls, image processing, data processing, decision making, estimation, modeling, and robotics. The fuzzy logic systems provide complex mappings from inputs to outputs, and this benefit usually results in better performances in comparison to non-fuzzy counterparts. Due to this, the fuzzy logic controllers have been applied to numerous challenging control problems for decades. Nowadays, more attention has been given to a new research direction of the fuzzy sets and systems, the general type-2 fuzzy logic controllers, which is the main motivation of this thesis. The internal structures of a class of Takagi-Sugeno-Kang type fuzzy logic controllers are first examined in detail. In this context, three fuzzy logic controller types (type-1, interval type-2, and general type 2) and two kinds of controller configurations (single-input and double-input) are considered. The baseline controllers, i.e. type-1 and interval type-2 fuzzy logic controllers, are presented in the preliminaries section. The fuzzy sets, fuzzy relations, fuzzy rules, fuzzy operators, and PID forms of these fuzzy logic controllers are explained in detail. The design assumptions and design parameters are given, also the most common design approaches are listed. Afterward, the general type-2 fuzzy sets and the general type-2 fuzzy logic controllers are presented. The general type-2 fuzzy logic controllers are described with α-plane associated horizontal slices because the α-plane representation provides useful advantages on the handling of the secondary membership function of the general type-2 fuzzy sets and the calculation of the general type-2 fuzzy logic controller output. It is shown that the α-plane based general type-2 fuzzy logic controller output calculation is accomplished through the well-known interval type-2 fuzzy logic computations. The secondary membership functions are further detailed in terms of their mathematical definitions and design options. The structure analysis on the general type-2 fuzzy sets shows the interactions between non-fuzzy, type-1 fuzzy, interval type-2 fuzzy, and general type-2 fuzzy sets happen in the secondary membership function. It is shown that the general type-2 fuzzy logic controller can easily transform into interval type-2 fuzzy, or type-1 fuzzy counterparts based on the secondary membership function definitions. As an outcome of this structural analysis, a new representation of the trapezoid secondary membership function is proposed based on a novel parameterization of the parameters that form the trapezoid shape. It is shown that the parameterized trapezoid secondary membership function is capable to construct trapezoid, triangle, interval, and singleton shapes so that the general type-2 fuzzy logic controllers are further capable to transform into interval type-2 fuzzy, or type-1 fuzzy counterparts. It is also shown that the proposed parameterization of the trapezoid secondary membership functions allows designing the control curves/surfaces of the general type-2 fuzzy logic controllers with a single tuning parameter. Moreover, the structural design suggestions are presented not only to construct fuzzy controllers in a straightforward manner but also to ease the design of the controllers with few design parameters. The design parameters of the general type-2 fuzzy logic controllers are grouped as the shape and the sensitivity design parameters with respect to their effects on the accuracy and the shape of the resulting fuzzy mapping. Accordingly, the tuning parameter of the secondary membership functions and the total number of α-planes are interpreted and as the sensitivity and shape design parameters, respectively. The shape analyses of the general type-2 fuzzy logic controllers show the effects of the proposed shape design parameter on the control curves/surfaces. In this context, the resulting fuzzy mappings of single input and double input general type-2 fuzzy logic controller structures are compared for various design settings of the shape design parameter. The comparative analyses provide interpretable and practical explanations on the potential advances of the shape design parameter. Based on the shape analyses, novel design approaches are proposed to tune the shape design parameter in a systematic way. In this context, it is suggested constructing the general type-2 fuzzy logic controllers over their type-1 and interval type-2 baselines and tuning them via the shape design parameter by providing a tunable tradeoff between robustness and performance. Therefore, it is aimed to combine benefits of baseline type-1 (relatively more aggressive control curves/surfaces better performance measures) and interval type 2 (relatively smoother control curves/surfaces, better robustness measures) fuzzy logic controllers. To enhance the control performance, two scheduling mechanisms are also proposed for online-tuning of the shape design parameter with respect to the steady-state operating points as well as transient-state dynamics. The sensitivity analyses of the general type-2 fuzzy logic controllers show the effects of the proposed sensitivity design parameter on the accuracy of the control curves/ surfaces. In this context, the resulting fuzzy mappings of single input and double input general type-2 fuzzy logic controller structures are also compared for various design settings of the sensitivity design parameter. The comparative sensitivity analyses show interpretable and practical explanations of the sensitivity design parameter in terms of calculation accuracy and computation burden. Therefore, it is suggested tuning the sensitivity design parameter by considering the limitations of hardware components such as resolution and processing speed. To accomplish the design in accordance with a tradeoff between sensitivity and computational time, a novel iterative algorithm is proposed to tune the sensitivity design parameter. The simulation and real-time experimental control studies validate the proposed design recommendations, systematic design approaches, and tuning methods for the general type-2 fuzzy logic controllers on benchmark control systems. In these control studies, the general type-2 fuzzy logic controllers are designed based on the proposed design methods. In order to show the performance improvements on the control systems, the general type-2 fuzzy logic controllers (tuned either online or offline) are compared with type-1 fuzzy and interval type-2 fuzzy counterparts. The performance measures clearly show that the online-tuned general type-2 fuzzy logic controllers outperform all general type-2, interval type-2, and type-1 counterparts on account of the proposed scheduling mechanisms over the proposed systematic design rules. The results also show that the systematic design of the general type-2 fuzzy logic controllers is simply accomplished by following the proposed tuning steps of the shape and sensitivity design parameters.
  • Öge
    Intelligent control system design and deployment for fuel cell air supply systems
    (Graduate School, 2024-06-10) Kendir, Fatih ; Kumbasar, Tufan ; 504192101 ; Control and Automation Engineering
    The mobility industry invests in intensive research and development programs to find a sustainable energy source without polluting the environment. A fuel cell system is an electrochemical device that generates electricity and is one of the promising energy sources. However, the commercialization of fuel cell systems is limited due to their lifespan. In this thesis, an intelligent control system has been designed and deployed for fuel cell air supply systems to enhance the net output power and mitigate the degradation due to oxygen starvation, resulting in a longer cell lifespan. To minimize the risk of starvation, more air than needed for chemical reactions is supplied to the fuel cell system. Here, the ratio between supplied and consumed oxygen amounts is defined as the excess ratio. The net output power of fuel cell systems deteriorates when the oxygen excess ratio is high, yet starvation occurs if the oxygen excess ratio is low. Therefore, an accurate control of oxygen excess ratio is crucial to not only maximize the net output power but also reduce the risk of starvation to mitigate cell degradation. To address this challenge, a 2DOF control structure fused with artificial intelligence is proposed in this thesis. The proposed control system involves a data-driven reference generator, a feedforward controller, and a feedback controller. The data-driven reference generator calculates the setpoint value of the oxygen excess ratio. On the other hand, the data-driven feedforward controller calculates the open-loop control signal to anticipate known system dynamics for improving control performance and reducing the control effort by the feedback controller. A PI controller is used as the feedback controller to track the desired setpoint value and calculate the closed-loop control signal. Then, the sum of open-loop and closed-loop control signals is applied to the compressor motor as a voltage input. A fuel cell system was simulated for various current loads and oxygen excess ratio values at the optimal stack temperature to understand the characteristics of fuel cell systems. The results showed that each stack current maximizes the net output power for a specific oxygen excess ratio. The relationship between the stack current and oxygen excess ratio that produces maximum net output power is highly nonlinear, which is challenging to model via traditional lookup-based solutions. Similarly, the compressor voltage needed to reach the optimal oxygen excess ratio, maximizing the net output power and stack current, also has a complex relationship. Therefore, data-driven reference generators and feedforward controllers are considered for learning the complex characteristics of fuel cell systems. The data to learn the data-driven models is acquired through steady-state analysis. Firstly, the data for single input models where the stack current is the only input of data-driven models is acquired by alternating the current and oxygen excess ratio at optimal stack temperature. Moreover, the stack temperature is changed by considering possible temperature fluctuations around the optimal stack temperature, and its effect on net power output is investigated. The results show that the net output power significantly changes with stack temperature. Therefore, it needs to be considered in the design of data-driven models. In this manner, the double-input models are designed with stack current and temperature inputs. The data-driven reference generator and feedforward controller for single and double-input models are learned via fuzzy models and neural networks. Various internal configurations of fuzzy models and neural networks have been studied to investigate their effects on modeling accuracy. The fuzzy models were constructed with various membership functions and learned through various techniques. On the other hand, different activation functions were utilized to build the neural network models. Moreover, the learning data was pre-processed through standardization and normalization to examine their effect on learning performance. Besides, polynomial regression-based reference generators and feedforward controllers were learned for performance comparison. Even though the learning performances of data-driven reference generators and feedforward controllers are pretty satisfactory compared to polynomial regression-based models, their contribution to net output power and degradation must be shown. In this manner, the proposed artificial intelligence fused 2DOF control system was simulated with various fuzzy models and neural networks as the reference generator and feedforward controller. A PI controller was utilized as the closed-loop controller. The PI controller coefficients were tuned through an iterative trial-and-error method in a defined operating point. The same PI controller was employed during the simulations of each design variant to have a fair comparison. In addition, a 1DOF control system was designed to expose the contribution of the 2DOF control structure. To assess the test results, evaluation criteria for the net output power and oxygen excess ratio were defined. In the evaluations, the tracking error and settling time of both targets were considered. In addition, a degradation model that depends on oxygen starvation was created to assess the contribution of data-driven models on cell life. A set of operation points depending on stack current was developed to test the proposed control system. Moreover, stack temperature changes were considered to assess the performance of the proposed control system under disturbances. The results showed that the proposed intelligent control system with fuzzy models and neural networks could efficiently track the desired oxygen excess ratio. Thanks to the data-driven models, the high-performing oxygen excess ratio control structure increases the net output power of fuel cell systems and reduces cell degradation due to oxygen starvation. In brief, the intelligent control system proposed in this thesis is a promising development in fuel cell systems to enable their widespread usage as a clean and sustainable energy source.