Intelligent Control Technologies

Major: Computerized Control Systems and Automatics
Code of subject: 7.151.06.O.001
Credits: 4.00
Department: Computerized Automatic Systems
Lecturer: Nakonechnyi M.V.
Semester: 1 семестр
Mode of study: денна
Мета вивчення дисципліни: The purpose of studying the academic discipline The purpose of studying the discipline "Intelligent control technologies" is to provide students with the necessary theoretical knowledge and practical skills in using computer technologies to build automatic control systems, which are based on achievements in the field of neural networks and fuzzy logic.
Завдання: The study of an academic discipline involves the formation and development of students' competencies: general: 1. The ability to choose the optimal option for its solution based on the analysis of the given task. 2. Ability to apply acquired knowledge in practice. 3. Have research skills. 4. The ability to solve tasks and make appropriate decisions. 5. Knowledge of a technical foreign language. 6. Ability to system thinking. 7. Responsibility for the quality and timeliness of the work performed. professional: 1. The ability to use modern computer technologies to solve the problems of object identification and synthesis of controllers. 2. The ability to choose a structural diagram of an automatic control system. 3. The ability to choose the type and structure of the controller based on the analysis of the characteristics of the control object. 4. The ability to choose the optimal learning algorithm of a neural network, based on the conditions for ensuring the specified speed and minimum learning error. 5. The ability to choose the type and number of functions belonging to a controller based on elements of fuzzy logic based on expert evaluations.
Learning outcomes: The purpose of studying the academic discipline The purpose of studying the discipline "Intelligent control technologies" is to provide students with the necessary theoretical knowledge and practical skills in using computer technologies to build automatic control systems, which are based on achievements in the field of neural networks and fuzzy logic. Tasks of the academic discipline The task of the course is to teach students to identify control objects and synthesize controllers using modern technologies based on the use of artificial neural networks and fuzzy logic. As a result of studying the academic discipline, the student should be able to demonstrate the following learning outcomes: 1. Know the features of building automatic control systems using neural networks and fuzzy logic. 2. To know the peculiarities of the implementation of the architecture of neural networks, based on the selected control laws. 3. Know the methods and algorithms of learning neural networks. 4. Based on expert evaluations of control processes, to know the features of the selection of functions belonging to controllers, made on the basis of elements of fuzzy logic. 5. Have problem-solving skills in the MATLAB (SIMULINK) environment. The study of an academic discipline involves the formation and development of students' competencies: general: 1. The ability to choose the optimal option for its solution based on the analysis of the given task. 2. Ability to apply acquired knowledge in practice. 3. Have research skills. 4. The ability to solve tasks and make appropriate decisions. 5. Knowledge of a technical foreign language. 6.Ability to system thinking. 7. Responsibility for the quality and timeliness of the work performed. professional: 1. The ability to use modern computer technologies to solve the problems of object identification and synthesis of controllers. 2. The ability to choose a structural diagram of an automatic control system. 3. The ability to choose the type and structure of the controller based on the analysis of the characteristics of the control object. 4. The ability to choose the optimal learning algorithm of a neural network, based on the conditions for ensuring the specified speed and minimum learning error. 5. The ability to choose the type and number of functions belonging to a controller based on elements of fuzzy logic based on expert evaluations. The learning outcomes of this discipline detail the following program learning outcomes: 1. The ability to create models of objects based on neural networks for the purpose of their identification. 2. The ability to develop controller structures based on neural networks or elements of fuzzy logic to implement given control laws. 3. The ability to choose methods and algorithms for learning neural networks in order to minimize their learning errors. 4. The ability to choose the functions of belonging to controllers made on the basis of elements of fuzzy logic, in order to ensure the specified characteristics of the automatic control system. 5. Ability to analyze the operation of the automatic control system using the MATLAB (SIMULINK) system.
Required prior and related subjects: Information theory. Digital control systems Theory of automatic control. Methods and means of modeling objects and systems
Summary of the subject: The educational discipline "Intelligent control technologies" considers methods of building automatic control systems using artificial neural networks and elements of fuzzy logic, analyzes the main approaches to building and training such networks, as well as algorithms for their implementation.
Опис: The study of an academic discipline involves the formation and development of students' competencies: general: 1. The ability to choose the optimal option for its solution based on the analysis of the given task. 2. Ability to apply acquired knowledge in practice. 3. Have research skills. 4. The ability to solve tasks and make appropriate decisions. 5. Knowledge of a technical foreign language. 6. Ability to system thinking. 7. Responsibility for the quality and timeliness of the work performed. professional: 1. The ability to use modern computer technologies to solve the problems of object identification and synthesis of controllers. 2. The ability to choose a structural diagram of an automatic control system. 3. The ability to choose the type and structure of the controller based on the analysis of the characteristics of the control object. 4. The ability to choose the optimal learning algorithm of a neural network, based on the conditions for ensuring the specified speed and minimum learning error. 5. The ability to choose the type and number of functions belonging to a controller based on elements of fuzzy logic based on expert evaluations.
Assessment methods and criteria: The educational discipline "Intelligent control technologies" considers methods of building automatic control systems using artificial neural networks and elements of fuzzy logic, analyzes the main approaches to building and training such networks, as well as algorithms for their implementation.
Критерії оцінювання результатів навчання: 100–88 points – (“excellent”) is awarded for a high level of knowledge (some inaccuracies are allowed) of the educational material of the component contained in the main and additional recommended literary sources, the ability to analyze the phenomena being studied in their interrelationship and development, clearly, succinctly, logically, consistently answer the questions, the ability to apply theoretical provisions when solving practical problems; 87–71 points – (“good”) is awarded for a generally correct understanding of the educational material of the component, including calculations, reasoned answers to the questions posed, which, however, contain certain (insignificant) shortcomings, for the ability to apply theoretical provisions when solving practical tasks; 70 – 50 points – (“satisfactory”) awarded for weak knowledge of the component’s educational material, inaccurate or poorly reasoned answers, with a violation of the sequence of presentation, for weak application of theoretical provisions when solving practical problems; 49-26 points - ("not certified" with the possibility of retaking the semester control) is awarded for ignorance of a significant part of the educational material of the component, significant errors in answering questions, inability to apply theoretical provisions when solving practical problems; 25-00 points - ("unsatisfactory" with mandatory re-study) is awarded for ignorance of a significant part of the educational material of the component, significant errors in answering questions, inability to navigate when solving practical problems, ignorance of the main fundamental provisions.
Recommended books: 1. A.A. Uskov, A.V., Kuzmin, Intellectual management technologies. M. Hotline - Telecom, 2004. 2. R. Kallan Basic concepts of neural networks Moscow, St. Petersburg, Kyiv., "Williams" publishing house, 2001. 3. P.F. Gogolyuk, T.M. Grechin Theory of automatic control, Lviv. Publishing House of Lviv Polytechnic National University, 2009. 4. Medvedev V.S. Neural networks. MATLAB 6 / Medvedev V.S., Potemkin V.G. - M: DIALOG-MYTHS, 2002. - 496p. 5. Sigeru O. Neurocontrol and ego applications: Translation. with English N. V. Batyna pod obsch. ed. A. I. Galushkina and V. A. Ptichkina / O. Sigeru, K. Marzuky, Yu. Rubiya - M: IPRZHR, 2000. - 272 p.: (Series "Neurocomputers and their application". Book 2). 6. Bodyansky E. V. Artificial neural networks: architecture, training, application / Bodyansky E. V., Rudenko O. G. - Kharkiv: Telemekh, 2004. 7. Komashinsky V.I. Neural networks and their application in control and communication systems / Komashinsky V.I., Smirnov D.A. // Hotline - Telecom. - 2002.–94p. 8. Osovsky S. Neural networks for information processing / Trans. from the Polish I.D. Rudynsky. M.: Finance and Statistics, 2002. – 344 p. ; Il. 9. Khaikin S. Neural networks: full course: Per with English. N.N. Kussul and A.Yu. Shelestova under the editorship. N.N. Kussul. - M.: Williams, 2006. - 1104 p.