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OPTIMIZATION OF A FUZZY CONTROL DESIGN WITH RESPECT TO A PARALLEL MECHANISM WORKSPACE
Andrš, Ondřej ; Maga, Dušan (referee) ; Singule, Vladislav (referee) ; Březina, Tomáš (advisor)
The Ph.D. thesis is focused on using the fuzzy logic for control of a parallel manipulator based on a Stewart platform. The proposed mechanism makes possible to simulate the physiological movements of the human body and observe degradation processes of the cord implants. Parallel manipulators such as a Stewart platform represent a completely parallel kinematic mechanism that has major differences from typical serial link robots. However, they have some drawbacks of relatively small workspace and difficult forward kinematic problems. Generally, forward kinematic of a parallel manipulators is very complicated and difficult to solve. This thesis presents a simple and efficient approach to design simulation model of forward kinematic based on Takagi-Sugeno type fuzzy inference system. The control system of the parallel manipulator id based on state-space and fuzzy logic controllers. The proposed fuzzy controller uses a Sugeno type fuzzy inference system (FIS) which is derived from discrete position state-space controller with an input integrator. The controller design method is based on anfis (adaptive neuro-fuzzy inference system) training routine. It utilizes a combination of the least-squares method and the backpropagation gradient descent method for training FIS membership function parameters to emulate a given training data set. The proposed fuzzy logic controllers are used for the control of a linear actuator. The capabilities of the designed control system are shown on verification experiment.
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Fuzzy Neural Networks
González, Marek ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This thesis focuses on fuzzy neural networks. The combination of the fuzzy logic and artificial neural networks leads to the development of more robust systems. These systems are used in various field of the research, such as artificial intelligence, machine learning and control theory. First, we provide a quick overview of underlying neural networks and fuzzy systems to explain fundamental ideas that form the basis of the fields, and follow with the introduction of the fuzzy neural network theory, classification and application. Then we describe a design and a realization of the fuzzy associative memory, as an example of these systems. Finally, we benchmark the realization using the pattern recognition and control tasks. The results are evaluated and compared against existing systems.
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Robustnost fuzzy řízení
Hebelka, Marek
Hebelka M. Robustness of fuzzy control. Diploma thesis. Brno: Mendel University, 2023. The thesis deals with the assessment of the robustness of the fuzzy controller in the framework of removing the influence of disturbance variables in the form of a uniform unit step of 10, 20, 30 and 40% of the input signal and adjustment of time constants by 5, 10, 15 and 20% of the original value. The thesis describes the basics of fuzzy control, simulation tools, classic controllers, and plants. As part of the work, the settings of individual systems are subsequently described and the effects of the key parameters of the regulated systems are analyzed. The resulting work is an evaluation and assessment of the robustness of the regulators in the control systems evaluated.
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Fuzzy Neural Networks
González, Marek ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This thesis focuses on fuzzy neural networks. The combination of the fuzzy logic and artificial neural networks leads to the development of more robust systems. These systems are used in various field of the research, such as artificial intelligence, machine learning and control theory. First, we provide a quick overview of underlying neural networks and fuzzy systems to explain fundamental ideas that form the basis of the fields, and follow with the introduction of the fuzzy neural network theory, classification and application. Then we describe a design and a realization of the fuzzy associative memory, as an example of these systems. Finally, we benchmark the realization using the pattern recognition and control tasks. The results are evaluated and compared against existing systems.
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OPTIMIZATION OF A FUZZY CONTROL DESIGN WITH RESPECT TO A PARALLEL MECHANISM WORKSPACE
Andrš, Ondřej ; Maga, Dušan (referee) ; Singule, Vladislav (referee) ; Březina, Tomáš (advisor)
The Ph.D. thesis is focused on using the fuzzy logic for control of a parallel manipulator based on a Stewart platform. The proposed mechanism makes possible to simulate the physiological movements of the human body and observe degradation processes of the cord implants. Parallel manipulators such as a Stewart platform represent a completely parallel kinematic mechanism that has major differences from typical serial link robots. However, they have some drawbacks of relatively small workspace and difficult forward kinematic problems. Generally, forward kinematic of a parallel manipulators is very complicated and difficult to solve. This thesis presents a simple and efficient approach to design simulation model of forward kinematic based on Takagi-Sugeno type fuzzy inference system. The control system of the parallel manipulator id based on state-space and fuzzy logic controllers. The proposed fuzzy controller uses a Sugeno type fuzzy inference system (FIS) which is derived from discrete position state-space controller with an input integrator. The controller design method is based on anfis (adaptive neuro-fuzzy inference system) training routine. It utilizes a combination of the least-squares method and the backpropagation gradient descent method for training FIS membership function parameters to emulate a given training data set. The proposed fuzzy logic controllers are used for the control of a linear actuator. The capabilities of the designed control system are shown on verification experiment.
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Automatické dokazování ve fuzzy logikách
Cintula, Petr ; Navara, M.
Computer algebra allows to perform many operations which were considered difficult, e.g., factorization, integration, symbolic solution of ODEs, etc. Logical operations are not always implemented. E.g., Maple 9 has a package LOGIC which was missing in several preceding versions. Except for packages for fuzzy control, there seems to be no professional software for fuzzy logical tasks. Here we summarize current situation in computer algebra support of testing tautologies in fuzzy logics.
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