DESCRIPTION OF THE COURSE - oldweb.tu-sofia.bgoldweb.tu-sofia.bg/eng_new/ECTS/ectas/ectas12-13/fa/MAICE_EN/char... · To develop skils for description of uncertain systems, ... mode

DESCRIPTION OF THE COURSE Name of the course Robust and optimal control

Code: MAICE01 Semester: 1

Type of teaching: Lectures (L) Laboratory work (Lab.)

Lessons per week: L – 2 hours Lab. – 2 hours

Number of credits: 5

LECTURER: Prof. P. Petkov (FA) - tel. 965 3457, еmail: [email protected]

Technical University of Sofia

COURSE STATUS IN THE CURRICULUM: Compulsory for the students speciality Automation, Information and Control Engineering, MEng programme, Faculty of Automation.

AIMS AND OBJECTIVES OF THE COURSE: To give knowledge on the modern methods for analysis and design of robust and optimal control systems. To develop skils for description of uncertain systems, robust stability and robust performance analysis, to perform H∞ design and µ -synthesis of multivariable control systems. To develop practical skils for using MATLAB in the robust analysis and design of control systems.

DESCRIPTION OF THE COURSE: The main topics concern: Properties of multivariable feedback systems, application of the singular values in the analysis of multivariable systems, H2 and H∞ norms of transfer matrices, uncertainty description, application of the linear fractional transformations, obtaining of unstructured and structured uncertainty models, properties of the structured singular value, robust stability and robut performance, mixed sensitivity H∞ design, H∞ loop shaping, µ synthesis and D-K iterations. Program language – MATLAB.

PREREQUISITES: Technical Mechanics, Theoretical Electrotechnics I, II, Control Theory I, II

TEACHING METHODS: Lectures, laboratory work from laboratory manual, work in teams, protocols preparation and defence.

METHOD OF ASSESSMENT: Written final examination (80%); laboratory work (20%).

INSTRUCTION LANGUAGE: Bulgarian

BIBLIOGRAPHY: 1. D.-W. Gu, P.Hr. Petkov, M.M. Konstantinov (2012), Robust Control Design with MATLAB®. Springer London.

DESCRIPTION OF THE COURSE Name of the course Robotics

Code: MAICЕ02 Semester: 1




LECTURER:

Assoc. Prof. Ph.D. V. Zamanov (FA) – tel.: 965 2738, email: [email protected] Technical University of Sofia

COURSE STATUS IN THE CURRICULUM: Compulsory for the students specialty Automation, Information and Control Engineering, MEng programme .

AIMS AND OBJECTIVES OF THE COURSE: Teaching the students the bases of contemporary robotics. The student must know to model robot manipulators and to know the structure, geometry, kinematics, proper functions and dynamics of robotic manipulators with open spatial kinematics chain. To acquire the knowledge in mechanics, motion control and application of mobile robots DESCRIPTION OF THE COURSE: The course starts with brief introduction in robotics. The accent is placed on group of questions about modeling of Robotic Manipulators. Inside is discussed: the structure, geometry, kinematics and dynamics of manipulator systems with open spatial kinematical chain. Special attention is taken to bases and methods used for solving the essential tasks in kinematics and modeling of the dynamics. Inside can be found question based on planning and control of manipulators motions. In the course is made ananalyze of parallel manipulators and multi arm manipulators. Analyze of specialized mobile robotic systems used for incident works, repair and control, research work. An essential place is given on walking robots. Several problems are treated in more detailed way: stability of position, coordination of movement and also movement in hops or craws.Laboratory work is focused in structural and geometrical analysis of some typical manipulators PUMA, SCARA thought on laboratory or real industry robots. Students create robot models with RobotAssist. Laboratory work is done with 5OWMoR, 2TraMoR and WR12.

PREREQUISITES: Technical mechanics, Technical means for automation, Control Theory,

TEACHING METHODS: Lectures, using slides, video presentations, case studies, laboratory, work in teams, protocols. METHOD OF ASSESSMENT: Two one-hour assessments at mid and end of semester (30+40%), laboratories (30%). INSTRUCTION LANGUAGE: Bulgarian BIBLIOGRAPHY: 1. Zamanov V., Karastoianov D., Sotirov. Z. , Mechanics and control of robots, Sofia,1993; 2. Craig J. J., Introduction to Robotics: Mechanics and Control (3rd Edition), Prentice Hall, NJ, 2004; 3. Roland Siegwart and Ilah Nourbakhsh, Introduction to Autonomous Mobile Robots, MIT Press, April 2004; 4. Mobile Robots - State of the Art in Land, Sea, Air, and Collaborative Missions, Edited by X.Q. Chen, Y.Q. Chen, and J.G. Chase, In-Teh, Viena, Austria, 2009. p.346.

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DESCRIPTION OF THE COURSE

Name of the course: Bioinformatics


Type of lectures: Lectures (L) Laboratory work (Lab.)



LECTURER:

Assos. Prof. Ph.D Dimitar Andonov Nenov (FА), phone.: 965-25-96, e-mail: [email protected] Technical University of Sofia

COURSE STATUS IN THE CURRICULUM: Compulsory for the students specialty Automation, Information and Control Engineering, MEng program, Faculty of Automation.

AIMS AND OBJECTIVES OF THE COURSE: The students should be able to apply methods for modelling and analysis of complex time variant systems and use statistical analysis software for solving engineering problems in the area of bioinformatics.

DESCRIPTION OF THE COURSE: The main topics concern: enzyme kinetics, mathematical models of the different enzyme kinetics schemes and its application. Different enzyme electrical - chemical systems their mathematical description and modelling of the modes of the work, technical and metrological characteristics. The application of the information technologies in area of the biological structures. The special topics of the this coure of lectures are: Statistical structures, dara analysis of the primary and secondary structure of the DNA molecule and protein structures. Bioanalysers and its application in biotechnology, medicine food industry , ecology and control of the biotechnological processes. All investigations are solved by the MATLAB environmental.

PREREQUISITES: Control Theory, Elements of Industrial Automation, Measurement of non -electrical variables, Estimation and Control of the biotechnological processes.

TEACHING METHODS: Lectures, using slides, case studies, laboratory, work in teams, protocols. Examination with two questions.

METHOD OF ASSESSMENT: Two one-hour assessments at mid and end of semester (80%), laboratories (20%).

INSTRUCTION LANGUAGE: Bulgarian.

BIBLIOGRAPHY: 1. Gibas C., Jambeck P. (2001), Bioinformatics Computer Skills, O’Reilly, 2001. 2. Baldi P., Brunak S. (1998), Bioinformatics. The Mashine Learning Approach. MIT Press, 1998. 3. Jagota A. (2000), Data Analysis and Cassifiucation for Bioinformatics, University of California, Santa Cruz, 2000. 4. Brown St. (2000), Bioinformatics: A Biologist’s Guide to Biocomputing and the Internet. Eaton Publishing, 2000.

List 1 1 Electric Drive Systems MAICE04.1 2 Pattern recognition theory MAICE04.2 3 Energy-saving Process Control MAICE04.3 4 Measurements and Testing for Electromagnetic Compatibility MAICE04.4 5 Machine Vision Systems MAICE04.5 6 Linear control systems MAICE04.6


Name of the course Electric Drive Systems

Code: MAICE04.1 Semester: 1




LECTURER: Assoc. Prof. Ph.D. M. Mikhov (FA) – tel.: 965 2948, еmail: [email protected]


COURSE STATUS IN THE CURRICULUM: Optional for the specialty of Automatics, Infor-matics and Control Engineering, Master Degree.

AIMS AND OBJECTIVES OF THE COURSE: To extend and deepen knowledge acquired at previous level of education about modern electrical drive systems. To acquaint students with differ-ent types of electric drives with high level of static and dynamic performance.

DESCRIPTION OF THE COURSE: Major topics covered: Electric drive systems with perma-nent magnet synchronous motors – features, methods for determination of rotor position, hysteresis current control, principles for development of drives with brushless DC motors; Electric drive sys-tems with hybrid stepping motors – features, performance improvement, control in microstepping mode of operation, closed systems of control in compliance with the rotor position; Electrical drive systems with switch reluctance motors - features, methods for determination of rotor position, prin-ciples for development, Systems of synchronized electric drives – control of dual- and multi-motor drives, synchronization of the main regulated coordinates and their ratios, accuracy evaluation; Sys-tems of numerical-program control – methods for interpolation, connection of electric drives with the control system. PREREQUISITES: Control of Electromechanical Systems, Electric Dives Theory, Power and Con-trol Electronics in Electric Drives, Control of Electric Drives, TEACHING METHODS: Lectures visually illustrated, Laboratory work with protocols prepara-tion. METHOD OF ASSESSMENT: Final written exam. INSTRUCTION LANGUAGE: Bulgarian. BIBLIOGRAPHY: 1. Mikhov M., Electric Drive Systems, Technical University of Sofia, Sofia, 2011, ISBN 978-954-438-922-2. 2. Acarnley P., Stepping motors: a guide to theory and practice, IEE, London, 2002, ISBN 978-085-296-029-5. 3. Hanselman D., Brushless Permanent Magnet Motor Design, University of Maine, Orono, 2006, ISBN 1-881855-15-5. 4. Boldea I., S. Nasar, Electric drives, CRC Press, Boca Ra-ton, 1999, ISBN 0-8493-2521-8. 5. Krishnan R., Switched reluctance motor drives, Modeling, Simulation, Analysis, Design, and Application, Boca Raton, Florida, 2001, ISBN 0-8493-0838-0.




Name of the course: Pattern recognition theory


Type of lectures: Lectures (L) Laboratory work (Lab.)



LECTURER: Assos. Prof. Ph.D Dimitar Andonov Nenov (FА), phone.: 965-25-96, e-mail: [email protected]


COURSE STATUS IN THE CURRICULUM: Optional for the students specialty Automation, Information and Control Engineering, MEng program, Faculty of Automation.

AIMS AND OBJECTIVES OF THE COURSE: The students should be able to apply the obtained theoretical knowledge and methods to solve practical problems in the domain of Pattern recognition.

DESCRIPTION OF THE COURSE: The main topics concern: clasification methods based on the knowledge data and metric , discriminate, linear and non-linear regression. The following criteria are uses for decision making: least of square, markov least of square, maximum of the likelihood, Bayes approach of the investigation. All investigations are solved by the MATLAB environmental.

PREREQUISITES: Control Theory, Elements of Industrial Automation, Measurement of non -electrical variables, Estimation and Control of the biotechnological processes.

TEACHING METHODS: Lectures, using slides, case studies, laboratory, work in teams, protocols. Examination with two questions.

METHOD OF ASSESSMENT: Two one-hour assessments at mid and end of semester (75%), laboratories (25%).


BIBLIOGRAPHY: 1. Ту Дж., Гонсалес. (1978), Принципы расспознавания образов, Мир, М. 1978. 2. Ахо Я. Дж. Хопкрофт, Дж. Ульман. (1979), Построение и анализ вычислительных алгоритмов, Мир, М. 1979. 3. Гренадер, У. (1981), Лекции по теории образов. Синтез образов. Мир, М. 1981. 4. Гренадер, У. (1983), Лекции по теории образов. Анализ образов. Мир, М. 1983. 5. Гренадер, У. (1983), Лекции по теории образов. Регулярные структуры. Мир, М. 1983.

DESCRIPTION OF THE COURSE Name of the course Energy-saving Process Control





LECTURER: Assoc. Prof. PhD Nina Nikolova (FA) - tel.: +359 965 34-89; +359 965 25-57,

еmail: [email protected] Assistant Momchil Borisov Rabadjiiski, (FA) - tel.: 965 29-42; [email protected]

Technical University of Sofia Assistant PhD Vesela Karlova-Sergieva, (FA) - tel.: 965 39-41; [email protected]


COURSE STATUS IN THE CURRICULUM: Optional for the students specialty Automation, Information and Control Engineering, MEng programme, Faculty of Automation

AIMS AND OBJECTIVES OF THE COURSE: The main objective of the course is to reveal possibilities for energy-saving in manufacture, through control optimization in means of energy criteria.

DESCRIPTION OF THE COURSE: Attention is paid to the methods for energy-saving control of industrial processes, based on the optimal control theory and fundamental laws of thermodynamics, with concern of product's quality. These methods reduce to steady-state and dynamic optimization; to control precision and to thermodynamic perfection of the processes through their control. The optimization in means of thermal and electrical energy in the transient mode is combined with their time-saving performance. Technically achievable solutions are examined, which are mainly intended for the industry - energetic industry, metallurgical industry, chemical industry etc., but also applicable for the everyday life - heating of buildings. A method for evaluation of their effectiveness is presented. PREREQUISITES: Background knowledge of Control Theory, Thermodynamics, Process Control Automation, Industrial Systems for Low-cost Automation. TEACHING METHODS: Lectures, using slides. Laboratory work on physical models of industrial installations, course assignment and with different software packs. METHOD OF ASSESSMENT: Written exam. INSTRUCTION LANGUAGE: Bulgarian BIBLIOGRAPHY: 1. Наплатаров К. (1999), Енергоикономично управление на процеси, София, Изд. на Технически Университет София, 1999.; 2.Наплатаров К., М. Рабаджийски (2007), РЛУ по Промишлени и енергоикономични системи - I част - учебно пособие, София, Изд. на Технически Университет София; 3. Наплатаров К., М. Рабаджийски (2007), РЛУ по Промишлени и енергоикономични системи за нискостойностна автоматизация - II част - учебно пособие; 4. Шински Ф. Управление процессами по критерию зкономии знергии, Москва,Мир, 1981; 5.Николов E. (2010), Робастно Фрактално Управление (предиктивни и алгебрични методи; системи с разпределени параметри), София 2010, 2010 Изд. Технически Университет София, ISBN -978-954-438-851-5, 375 стр.




DESCRIPTION OF THE COURSE Name of the course Measurements and Testing for Electromagnetic Compatibility





LECTURER:

Assoc. Prof. Ph.D. Nikolai Panteleev-IICT-BAS,BIM tel 0888 83 95 24; [email protected]

COURSE STATUS IN THE CURRICULUM: Elective course for the students specialty Automatics, Information and Control Technics, МEng programme of Faculty of Automatics

AIMS AND OBJECTIVES OF THE COURSE: The main objective of the course is to introduce requirements and methodology for measurement and testing of electromagnetic compatibility to the students. Taking this course will develop skills and knowledge for measurement and testing of electromagnetic compatibility. DESCRIPTION OF THE COURSE: The main topics concern: Introduction to Electromagnetic compatibility. Electromagnetic environment. Sources and parameters of electromagnetic disturbances. Directive 2004/108/ЕС. Assessment of device compliance to the EMC Directive. Standards in accordance to the EMC Directive. Methods and means for measurement and testing of disturbances. Uncertainty of measurements. Requirements for EMC testing laboratories. EMC biological aspects. Methods for ensuring electromagnetic compatibility of devices – shielding, grounding, power supply filters, watch dog methods.

PREREQUISITES: Electric measurements. Theoretical Bases of Electrical Engineering.

TEACHING METHODS: Lectures, using slides, laboratory work. METHOD OF ASSESSMENT: The assessment comprises of final exam (80%), laboratories (15%), participation during lectures and laboratory exercises (5%)


BIBLIOGRAPHY: 1. А.Лазаров, Електромагнитна съвместимост на средствата за измерване и управление, ТУ-София, 2004. 2. Гурвич И.С. Защита ЭВМ от внешних помех, Энергоатомиздат, 1984. 3. Дж. Барнс Електронное конструирование: Методы борбы с помехами, Мир, 1990. 4. Хенри От, Методи за намаляване на шумовете в електронните системи, Държавно издателство "Техника", София, 1979 г. 5. Reinaldo Perez, Handbook of Electromagnetic compatibility Academic Press1995 ISBN 0-12-550710-0. 6. Други източници: стандарти, списания, интернет и др.


DESCRIPTION OF THE COURSE Name of the course Machine Vision Systems





LECTURER: Assoc. Prof. Ph.D. Pencho Venkov (FA) – tel.: 02 965 3735, email: [email protected]


COURSE STATUS IN THE CURRICULUM: Eligible for the students specialty "Automatics, Information and Control Engineering" at the Master Eng. Program of the Faculty of Automatics

AIMS AND OBJECTIVES OF THE COURSE: At the end of the course the students are expected to be able to apply the Machine Vision Systems for the guidance of autonomous mobile robots in an industrial environment, to chose the TV cameras and lighting systems, to use and adapt software modules of the libraries for analysis and recognition of objects in the robot's surrounding

DESCRIPTION OF THE COURSE: The main topics concern: The choice of the TV cameras with CCD and CMOS transducers in the visible and near IR spectrum and the appropriate lighting systems. The choice of interface modules for standard and digital video signals (Frame Grabber, USB 2.0, IEEE 1394b, GigE, and Camera Link).The architecture of the PC based systems, smart cameras, industrial and embedded vision systems for industrial applications. The specialized software for pretreatment and analysis of visual images and for pattern recognition and positioning of objects in the environment. PREREQUISITES: Robotics, Elements of Industrial Automation, Electronics, Informatics, Physics, Signal Analysis. TEACHING METHODS: Lectures, using slides, case studies, laboratory work, work in teams, protocols practical work description and defence. METHOD OF ASSESSMENT: Exam (72%), laboratories work - (28%) INSTRUCTION LANGUAGE: Bulgarian BIBLIOGRAPHY: 1.Венков П., Анализ и разпознаване на изображения и сцени, Изд. на ТУ – София, 1996 г.;2. Венков П., Информационно-сензорни системи за роботи. Изд. на ТУ-София, 2000г.; 3.Г.Глухчев, П.Венков, Д.Мутафов, М.Янчева, Елементи на теорията за разпознаване на образи, Изд. на БАН, София, 1982г.; 4. К.Фу, Р.Гонсалес, К. Ли, Робототехника, Москва, "Мир", 1989.; 5. Б.Хорн, Зрение роботов, Москва, "Мир", 1989.; 6. А.Пъю, Техническое зрение роботов, Москва, "Машиностроение", 1987.; WWW адреси: Computer Vision Homepage; What Robots See; Applications of Machine Vision

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DESCRIPTION OF THE COURSE Name of the course Linear control systems





LECTURER: Assoc. Prof. Kamen Perev (FA) – tel.: 965 2452, email: [email protected]

Assoc. Prof. Andrey Yonchev (FA) – tel.: 965 2452, email: [email protected] Technical University of Sofia

COURSE STATUS IN THE CURRICULUM: Selective for the students in specialty “Automatics, information and control technology”, MEng program of the Technical University of Sofia

AIMS AND OBJECTIVES OF THE COURSE: At the end of the course the students will acquire basic, fundamental knowledge in the mathematical treatment of linear control systems and will be able to solve system analysis and design problems, which will give them the ability to conduct their own research in this area. DESCRIPTION OF THE COURSE: The students will learn the basics of contemporary linear system theory. The course presents a general approach for treatment of linear control systems by using linear operator theory. The basic concepts and results from the mathematical system theory are introduced. Both, the problems of system analysis and synthesis are presented. The students will acquire basic knowledge for the main analysis problems like stability, controllability and observability. The students will also learn how to design model predictive controllers. The laboratory exercises will help the students to obtain practical skills for exploring the most popular algorithms, which find their application in resolving linear system problems. The students will learn how to develop computer programmes and practice on the basic decomposition algorithms, which build the kernel of the software package of MATLAB. PREREQUISITES: Control theory II, MIMO control systems design TEACHING METHODS: Lectures using a black board; laboratory work using digital computers and the software package of MATLAB. METHOD OF ASSESSMENT: Written final exam during the examination session INSTRUCTION LANGUAGE: Bulgarian BIBLIOGRAPHY: 1. Маджаров, Н., Линейни системи за управление, Изд. ТУ-София, 1999 2. Петков, П., Многомерни системи за управление, Изд. ТУ-София, 1998 3. Стренг, Г., Линейная алгебра и еë применения , Мир, Москва, 1980 4. Kailath,T., Linear systems, Prentice-Hall, Englewood Cliffs, 1980 5. Callier, F., C. Desoer, Linear system theory, Springer-Verlag, New York, 1991 6. Rawlings, J., and D. Mayne, Model predictive control: Theory and design, Nob Hill Publ., 2009 7. Maciejowski, J., Predictive control with constraints, Harlow, Prentice Hall, 2002



List 2 1 Adaptive Control of Mechatronic Systems MAICE05.1 2 Stochastic Analysis of Bioprocess systems MAICE05.2 3 Systems analysis and strategic control MAICE05.3 4 Quality Management MAICE05.4 5 Adaptive Control of Mechatronic Systems MAICE05.5 6 Control of discrete event systems MAICE05.6


Name of the course Adaptive Control of Mechatronic Systems


Type of teaching: Lectures (L) Laboratory work (Lab.) Semester assignment (SA)



LECTURER: Assoc. Prof. Ph.D. Vassil Balavessov (FA) – tel.: 965 3258, email: [email protected]


COURSE STATUS IN THE CURRICULUM: Optional for the students specialty Automation, Information and Control Engineering, MSc programme, Faculty of Automation.

AIMS AND OBJECTIVES OF THE COURSE: The coarse aims at teaching the basic principles and techniques of adaptive control, as well as to specify its applications in mechatronic systems. At the end of the course the students are expected to know the adaptive control system specificities, to be able to solve engineering problems related to design of adaptive control, and to analyze and in-vestigate the performance using computer modelling and simulation..

DESCRIPTION OF THE COURSE: Basic techniques of adaptive control of mechatronic sys-tems and robots are studied. The main topics concern: Mechatronic systems: specificity of dynami-cal description, basic properties, control requirements; Non-adaptive control: advantages and draw-backs; Basic prerequisites for adaptive control; Direct adaptive control; Indirect adaptive control; Real-time parameter estimation; Self-tuning control: minimum variance, generalized minimum variance, and model-following control; Reference model adaptive control; Computed torque based adaptive control; Inverse model based adaptive control..

PREREQUISITES: Control Theory, Mathematics.

TEACHING METHODS: Transparencies and multimedia means are used for lecture delivery. The laboratory work is based on computer simulations, as well as on an operational robot and robot-ized machining centre. The course assignments relate to design, analysis and simulation of a par-ticular mechatronic system. METHOD OF ASSESSMENT: Continuous assessment. Two written open-book tests (40%), laboratory work (20%), course assignment (40%).

INSTRUCTION LANGUAGE: English

BIBLIOGRAPHY: [1] Исии, Т., Симояма, И., и др., Мехатроника, Москва, Мир, 1988. [2] Тер-тичный-Даури В. Ю. Адаптивная механика, Наука, Физматлит, Москва 1998. [3] Томов И. И., Систе-ми за оптимално и адаптивно управление (втора част), София, Изд. на ВМЕИ, 1991. [4] Вукобратович М., Стокич, Д., Кирчански Н. Неадаптивное и адаптивное управление манипуляционными роботами, Москва, Мир, 1989. [5] Astrom, K.-J., and Wittenmark, B., Adaptive Control, Addison-Wesley, 1989. [6] Isermann, R., Lachman, K.-H., And Matko, D., Adaptive Control Systems, Prentice Hall, 1992. [7] The Zodiac, Theory of Robot Control, C. C. De Wit, B. Siciliano, And G. Basten (Eds), Springer-Verlag, 1996.


DESCRIPTION OF THE COURSE Name of the course Stochastic Analysis of Bioprocess systems





LECTURER: Prof. Ph.D. S. Yordanova (FA) – tel.: 965 3313, email: [email protected]


COURSE STATUS IN THE CURRICULUM: Compulsory for the students specialty “Automatics, Information And Control Techniques” of the faculty “Automatics”, master degree.

AIMS AND OBJECTIVES OF THE COURSE: At the end of the course the students are expected to be able to apply the methodology for modelling of bioprocess systems by stochastic processes. The students acquire skills to investigate by simulation of the discrete and continuous probability processes in MATLAB and SIMULINK environmental and to use it in solving of engineering problems, analysis and validation of the results.

DESCRIPTION OF THE COURSE: The main topics concern: Methods for stochastic analysis of the bioprocess systems. Statistical structures. Statistics. Information in statistical structures. Statistical test of hypotheses. Statistical estimation. Stochastic processes. Classification and characteristics of the base stochastic processes. Markov stochastic processes. Stochastic differential equations. Stochastic description of the generalization stoicheometrics equations based on known yield coefficients and generalized reaction rates. Stochastic description of the generalization stoicheometrics equations when the yield coefficients and generalized reaction rates are unknown. Diffusion processes. Process of "birth and death". Description of generalization stoicheometrics and ecological system of type "predator - victim". Stochastic models of the DNA. Frequency and information analysis of the DNA. PREREQUISITES: Fermentation technologies, System identification, Estimation and control of biotechnological processes, Biosensors systems and analyzers, Data analysis and automation of the biotechnological processes. TEACHING METHODS: Lectures, using slides, case studies, laboratory and course work, work in teams, protocols and course work description preparation and defence. METHOD OF ASSESSMENT: Two one-hour assessments in the middle and end of the semester (62%), laboratories (18%), course work - two off assignments (20%) INSTRUCTION LANGUAGE: Bulgarian BIBLIOGRAPHY: 1. Gardiner К. В.,(1986), Stochastic methods in natural science, Moskow, Mir, 512. 2. Stoyanov Y.,(1978), Stochastic processes - theory and applications, Sofia, Nauka i izcustwo, 214. 3. Dayvis М. H. А.,(1984), Linear estimation and control, Moskaw, Nauka, 205. 4. Ross S. M.,(1991), Introduction to Probability Models, Academic Press. Inc., New York, p.499 .5. Guttorp P.,(1991), Statistical Inference for Branching Processes, John Wiley & Sons Inc., New York, p. 254.



Name of the course Systems analysis and strategic control



Lessons per week: L – 2 hours, Lab. – 2 hours.


LECTURER: Assoc. Prof. Ph.D. B. Kirilov (FA), tel.: 965 3941, email: [email protected]

Assoc. Prof. Ph.D. G. Sapundjiev (FA), tel.: 965 2940, email: [email protected] Technical University - Sofia

COURSE STATUS IN THE CURRICULUM: Optional for the students specialty Automation, Information and Control Engineering, MEng program, Faculty of Automation.

AIMS AND OBJECTIVES OF THE COURSE: After completion of the course the students are expected to be able to apply the methodology of systems analysis and to solve applied problems form the field of strategic control.

DESCRIPTION OF THE COURSE: The main topics concern: Systems analysis basis; Principles and main points of systems analysis; Classification, borders and system properties; The system as a model of the plant; Complexity; Fundamental cause analysis; Strategic control in case of antagonistic conflict; Strategy choosing in cases of antagonism; Strategy choosing in multi-stage antagonistic problems; Strategic control in case of non-antagonistic conflict; Strategy choosing in cases of non-antagonistic conflict; Choice of strategies in case of coalition (arbitrage and cooperative problems); Application of prediction methods in systems analysis and strategic control. PREREQUISITES: Systems design, Operations research, Decision-making in control systems.

TEACHING METHODS: Lectures, using slides, case studies, laboratory and course work, work in teams, protocols and course work description preparation and defence. METHOD OF ASSESSMENT: Two one-hour assessments at mid and end of semester (70%), laboratories (20%), course work - two assignments (10%)


BIBLIOGRAPHY: 1. Гиг, Дж. ван. (1981), Прикладная общая теория систем, М., Мир, 1981; 2. Клир, Дж. (1990), Системология, М., Радио и связь, 1990; 3. Gigch, J.P.van. (2003), Metadecisions: Rehabilitating Epistemology (Contemporary Systems Thinking), NY, Kluver Academic/Plenum Publishers, 2003; 4. Тарасенко, Ф.П. (2004), Прикладной системный анализ, Томск, Изд. Том. ун-та, 2004; 5. Новосельцев, В. И. и др. (2006),Теоретические основы системного анализа, М. „:Майор”, 2006; 6. Сапунджиев, Г. (2011), ,Стратегическо управление при конфликт и при коалиция, С., Изд.ТУ. 2011; 7. Сапунджиев, Г., М. Георгиев. (2006), Ръководство за упражнения и курсов проект по Стратегическо управление при конфликт и при коалиция, С., Изд.ТУ, 2006; 8. Диксит, Ав., Б.Нейлбъф. (2010), Изкуството на стратегията, С., Lokus, 2010; 9. Фатхутдинов, Р. (2010), Стратегический менеджмент. М., ЗАО, 2010; 10. Yu, P.L. (1998), Forming Winning Strategies: An Integrated Theory of Habitual Domains. N. York, Springer Verlag 1998.



DESCRIPTION OF THE COURSE Name of the course Quality Management





LECTURER:Assist. Prof. PhD Radoslav Deliyski (FA) – tel.: 965 3465, email:

[email protected]

Technical University of Sofia.

COURSE STATUS IN THE CURRICULUM: Elective course for the students specialty Automatics and Information and Control technics, МEng programme of Faculty of Automatics

AIMS AND OBJECTIVES OF THE COURSE:

At the end of the course the students are expected to be able to use theory of probability and statistics in quality control, to know the basic methods in construction of variable and attribute control charts, to define acceptance sample size and know the international quality management systems.

The aim of the subject is to acquaint students with the modern methods and tools for quality management. The students will get knowledge in area of quality management systems requirements as well as organizational improvement activities in manufacturing in all aspects.

DESCRIPTION OF THE COURSE: The main topics concern: Modern concepts of Total Quality Management (TQM); Fundamentals of statistical quality control, control charts for variables; Statistical Acceptance sampling; Variance, correlation and regression analysis, Engineering methods for quality control, Taguchi methods for quality control, Measurement devices, Control systems; CAQ - Computer integrated systems for quality management, International standards for quality systems; Certification of quality, Quality costs.

PREREQUISITES: Mathematics, Electrical measurements, Metrology, Metrological assurance.

TEACHING METHODS: Lectures, using power-point presentations, laboratory work using special software, course work description preparation and defence. METHOD OF ASSESSMENT: Two one-hour assessments at mid and end of semester (65%), laboratories (15%), course work assignment (20%) INSTRUCTION LANGUAGE: Bulgarian

BIBLIOGRAPHY: 1. Дюкенджиев Г., Р. Йорданов. Контрол и управление на качеството. Софтрейд, София, 2012. 2. Станчева В. Й., К. Я. Киров, Н. П. Стефанов. Управление на качеството. QM, Варна, 1995. 3. Besterfield, D., Quality Control, Prentice Hall, 2004. 4. Crosby, P., Quality js Free, McGraw-Hill Book Company, 1984. 5. Doming, W., Quality, Productivity, and Competitive Position, MIT, 1982. 6. Juran, J., Quality Control Handbook, MeGraw-Hill Book Company, 1974. 7. Hoyle, D., ISO - 9000 Quality Systems Handbook, Butterworth-Heinemann Ltd., 1994.


DESCRIPTION OF THE COURSE Name of the course Adaptive Control of Mechatronic Systems





LECTURER: Assoc. Prof. Ph.D. Vassil Balavessov (FA) – tel.: 965 3258, email: [email protected]


COURSE STATUS IN THE CURRICULUM: Optional for the students specialty Automation, Information and Control Engineering, MSc programme, Faculty of Automation.

AIMS AND OBJECTIVES OF THE COURSE: The coarse aims at teaching the basic principles and techniques of adaptive control, as well as to specify its applications in mechatronic systems. At the end of the course the students are expected to know the adaptive control system specificities, to be able to solve engineering problems related to design of adaptive control, and to analyze and investigate the performance using computer modelling and simulation..

DESCRIPTION OF THE COURSE: Basic techniques of adaptive control of mechatronic systems and robots are studied. The main topics concern: Mechatronic systems: specificity of dynamical description, basic properties, control requirements; Non-adaptive control: advantages and drawbacks; Basic prerequisites for adaptive control; Direct adaptive control; Indirect adaptive control; Real-time parameter estimation; Self-tuning control: minimum variance, generalized minimum variance, and model-following control; Reference model adaptive control; Computed torque based adaptive control; Inverse model based adaptive control.. PREREQUISITES: Control Theory, Mathematics. TEACHING METHODS: Transparencies and multimedia means are used for lecture delivery. The laboratory work is based on computer simulations, as well as on an operational robot and robotized machining centre. The course assignments relate to design, analysis and simulation of a particular mechatronic system. METHOD OF ASSESSMENT: Continuous assessment. Two written open-book tests (40%), laboratory work (20%), course assignment (40%). INSTRUCTION LANGUAGE: English BIBLIOGRAPHY: [1] Исии, Т., Симояма, И., и др., Мехатроника, Москва, Мир, 1988. [2] Тертичный-Даури В. Ю. Адаптивная механика, Наука, Физматлит, Москва 1998. [3] Томов И. И., Системи за оптимално и адаптивно управление (втора част), София, Изд. на ВМЕИ, 1991. [4] Вукобратович М., Стокич, Д., Кирчански Н. Неадаптивное и адаптивное управление манипуляционными роботами, Москва, Мир, 1989. [5] Astrom, K.-J., and Wittenmark, B., Adaptive Control, Addison-Wesley, 1989. [6] Isermann, R., Lachman, K.-H., And Matko, D., Adaptive Control Systems, Prentice Hall, 1992. [7] The Zodiac, Theory of Robot Control, C. C. De Wit, B. Siciliano, And G. Basten (Eds), Springer-Verlag, 1996.



Name of the course: Control of discrete event systems





LECTURER: Assoc. Prof. Ph.D.K. Filipova (FA) - tel.: 965 2526, еmail: [email protected]

Technical University of Sofia COURSE STATUS IN THE CURRICULUM: Elective for the students specialty Automatics, Information and Control Technology, MEng programme of the Faculty of Automatics. AIMS AND OBJECTIVES OF THE COURSE: At the end of the course the students are expected to know the basic concepts of discrete event systems, methods and principles for formal description of devices for logic control; to apply the different Petri nets for modeling and analysis of the properties and quality of control; to make a verification of the models with Petri nets; to know the integration of MATLAB/ SIMULINK and Xilinx ISE Design Suite and to make automatic generation of HDL code and testbench; to use the VHDL code for automated design of control systems by different descriptions, in various enviroments and with different levels of hierarchy; to be aware of the methods and tools for synthesys of discrete event systems in programmable environment (FPGA and CPLD).

DESCRIPTION OF THE COURSE: The main topics concern: Discrete event systems – main topics, special features, description, characteristics. Petri nets (PN) as an instrument for modelling. Analysis of their characteristics. Verification strategies. Languages for description of hardware HDLs. Language constructions and operators. Syntactic rules for building of а VHDL description. Synthesis of logical elements and combinational logic functions by using VHDL. Synthesis of sequential logic circuits. Synthesis by VHDL of finite state machines by functional and algoritmic. Automatic generation of VHDL and Verilog code for synhthesis of machines. Description and synthesis of discrete event control system. Programmable devices - introduction, types of programmable logic – structures and estimation of their capabilities. Complex programmable logic devices (CPLD) and field programmable gate arrays (FPGA) – main topics, resources and comparative analysis.

PREREQUISITES: Mathematics, Logic control of processes and systems, Microprocessor control systems. TEACHING METHODS: Lectures, using slides, case studies, laboratory and course work, work in teams, protocols and course work description preparation and defence. METHOD OF ASSESSMENT: Two assessments at mid and end of semester (62%), laboratories (18%), course work (20%). INSTRUCTION LANGUAGE: Bulgarian BIBLIOGRAPHY: 1. Diaz M., Petri Nets: Fundamental Models, Verification and Applications, ISBN: 1848210795, 2009; 2. Cassandras C., Lafortune St., Introduction to Discrete Event Systems, ISBN: 1441941193, 2010; 3. Ashenden P.J., The Designer’s Guide to VHDL, 2002; 4.Filipova K., Hristov M., Using of (v)HDL for synthesis of electronic hardware, KING,Sofia, 2004; 5. Vahid F., Digital Design with RTL Design, VHDL, and Verilog, Second Edition, , ISBN 978-0-470-53108-2, 2010; 6. Girault C., Petri nets for systems engineering : a guide to modeling, verification and applications, Berlin , Springer, 2003; 7. Lee S. Advanced Digital Logic Design Using VHDL, State Machines, and Synthesis for FPGA's, ISBN: 0534466028, 2005.


List 3 1 Special electric drives MAICE06.1 2 Dynamical Systems in Biotechnologies MAICE06.2 3 Nonlinear and adaptive process control MAICE06.3 4 Metrology assurance MAICE06.4 5 Robots in medicine MAICE06.5 6 Digital Signal Processing MAICE06.6


Name of the course Special electric drives


Type of teaching: Lectures (L) Laboratory work (Lab.) Semester projects (SP)

Lessons per week: L – 2 hours Lab. – 1 hour


LECTURER:

Assoc. Prof. Ph.D. Boris Borisov (FA) – tel.: 965 3507, e-mail: [email protected]


COURSE STATUS IN THE CURRICULUM: Optional for the students specialty Automation, Information and Control Engineering, MEng programme, Faculty of Automation.

AIMS AND OBJECTIVES OF THE COURSE: The aim of the course is teaching of current electric drives with specific and focused application.

DESCRIPTION OF THE COURSE: The course addresses unique electric drives, broadly explor-ing current achievements of the power converting technology and microprocessor control: cascade control structures with asynchronous motor with coiled rotor, systems for synchronous rotation, multimotor drives with specific characteristics, stepwise drives. Attention is paid to the use of some special electric drives such as servo motors, resolvers, etc.

PREREQUISITES: Electromechanical devices, Power electronics in electric drives, Control of Electromechanical Systems, Control Systems of Electric Drives.

TEACHING METHODS: Lectures and Laboratory works.

METHOD OF ASSESSMENT: Continuous assessment. INSTRUCTION LANGUAGE: Bulgarian BIBLIOGRAPHY: 1. Ключев В. И., Теория электропривода, Москва, Энергоатомиздат, 2001 2. Копылов И.П., Электрические машины, Москва, Высшая школа, 2004 3. Ключев В.И. Теория на електрозадвижването, Техника, С.1989 4. Бертинов А.И. Специалные электрические машины, М. 1982 5. Чиликин М.Г. - Общий курс электропривода, М.1981



Name of the course: Dynamical Systems in Biotechnologies




Number of credits:5

LECTURER:

Stoyan Nedelchev Stoyanov, Ph.D., Associate Professor, Faculty of Automation Tel.: 840-30-45, e-mail: [email protected]

Technical University of Sofia COURSE STATUS IN THE CURRICULUM: Compulsory for the students specialty “Automa-tion, Information and Control Engineering”, “Faculty of Automation”, Sofia Technical University, education-qualification degree “Magister”. AIMS AND OBJECTIVES OF THE COURSE: The could be obtained the knowledges for theo-retical bases and practical methods in area of experimental investigations and automation of the bi-otechnological processes. DESCRIPTION OF THE COURSE: Topics to be covered: Nonlinear Mathematical Models of Dynamical Systems in Biotechnogies, Mathematical Models of Biotechnical Processes, Mathematical Models of Continuous and Fed-Batch Biotechnical Processes, Reduction of the General Dynamical Model, A General rule for order reduction, Stability Analysis of Biotechnical Processes, Stability Analysis of Nonlinear Biotechnical Processes, Equilibrium states, Multiple Equilibrium states, Bounded Input Bounded State Stability, Asymptotic Observers for State Estimation, State and Parameter Estimation with Unknown yield Coefficients, Asymptotic adaptive observer, Estimation of Biotechnological Variable, Software Sensors for Bioreactors, Ex-ponential Observability, Linearizing Control of Biotechnical Process, State and Parameter Estima-tion with Unknown Yield Coefficients, Adaptive Linearizing control, System Control of the Dis-solved oxygen concentration, Discrete-time observer-based estimater, Extremum Seeking Control of the Biotechnical Processes. PREREQUISITES: Control Theory, Basic of the Bioelectroengineering, Technical Elements of Industrial Automation, Measurement of Nonelectrical Variables, Fermentation of the Technologies, Bioatomation, Design of the Automation System for Biotechnical Processes, Control System of the Biotechnical Production and Automation System in Biotechnolidies. TEACHING METHODS: Lectures, Power Point slides, case studies, laboratory, work in teams, protocols, examinations with two questions. METHOD OF ASSESSMENT: Two one-hour exams at mid and end of the semesters (75% of the course grade), laboratories (25% of the course grade). INSTRUCTION LANGUAGE: Bulgarian. BIBLIOGRAPHY: 1. St. Tsonkov, St.Stoyanov, Ts.Georgiev, (1994), Control theory Basis of Biotech-nical Processes, С., ТУ, 1994 2.St.Tsonkov St., D.Filev, I.Simeonov, L.Vaklev, (1992), Control of Bio-technical Processes, S., Technika, 1992. 3.J. Staniskis., (1984), Optimal Control of Biotechnical Processes, Vilnius, Mokslas, 1984. 4. Д. Бейли, Д.Оллис, (1989), Основьi биохимической инженерии, М., 1989. 5. Birukov V., V. M. Kantare (1985) Optimisation of the batch fermentation processes, Mockow, Nauka, 1985. 6. S.Stoyanov, (2000), Robust Multiple-Input-Multiple-Output Control of Non-linear Continuous Fermentation Processes, Bioprocess Engineering, Springer, Germany, Vol. 23, No4, Oct. 2000, pp.309-315. 7.G. Bastin, D.Dochain, (1990), On-line Estimation and Adaptive Control of Bioreactors, Louvain, Belgium, 1990. 8. V.I.Utkin, (1981), Скользящие режимьi в задачах оптимизации и управления, М., Наука, 1981.


Name of the course Nonlinear and adaptive process control

Code: MAICE06.3

Semester: 1




LECTURERS: Assoc. Prof. Asen Todorov, Ph.D. (FA) – tel.: 965 34 05, e-mail: [email protected]

Assist. Prof. Stanislav Enev, Ph.D. (FA) – tel.: 965 39 41, e-mail: [email protected] Technical University of Sofia

COURSE STATUS IN THE CURRICULUM: Optional for the students specializing in Automation, Information and Control Engineering, MEng program, Faculty of Automation. AIMS AND OBJECTIVES OF THE COURSE: During the course, the students learn the basic methods of nonlinear and adaptive control fields. The nonlinear and time-varying properties and characteristics of industrial processes are brought to light and appropriate methods for control system design are introduced. DESCRIPTION OF THE COURSE: Time-varying industrial processes are analyzed from control point of view. The origin and nature of the time-variance are determined. Basic control structures and algorithms for adjusting controller parameters are studied. Problems related to algorithm convergence and stability of the overall adaptive control system are thoroughly analyzed. Based on different practical applications and case studies, the properties and characteristics of basic types of adaptive systems, i.e. model-reference adaptive systems (MRAS), self-tuning (ST) and others, are enlightened. Different applications of Lyapunov’s direct method for designing nonlinear and/or adaptive control systems are discussed. A group of methods for nonlinear control system design, based on the introduction of a linearizing state feedback are also considered.

PREREQUISITES:, Control Theory- 1 and 2 part, Identification, Process control, Elements of Industrial Automation.

TEACHING METHODS: Lectures with slides and presentations, Laboratory work with models, laboratory experiments and different modeling and simulation software packages. The course project concerns all stages in the design of an adaptive control system.

METHOD OF ASSESSMENT: Midterm examination(20%), written final examination, including problems (60%); laboratory work (20%), defence of course project.


BIBLIOGRAPHY: 1. Велев К. (1995), Адаптивно управление, София, 1995. 2. Томов Ил. (1990), Адаптивно и оптимално управление, София, Техника, 1990. 3. Острьом К., Б. Витенмарк. (1988), Адаптивно управление, 1988. 4. Sastry S., M. Bodson. (1989), Adaptive Control – Stability ,Convergence, and Robustness, Prentice-Hall 1989. 5. Slotine, J.J.E., W. Li. (1991), Applied Nonlinear Control. Englewood Cliffs. NJ: Prentice Hall, 1991. 6. Narendra K.S., A. Annaswamy. (2005), Stable Adaptive Systems. Dover Publications, NY, 2005.


DESCRIPTION OF THE COURSE Name of the course: Metrology assurance



Lessons per week: L–2 hours Lab.–1 hour


LECTURER:

Assoc. Prof. Ph.D. G. Milushev (FA) – tel.02965 2380, email: [email protected], Technical University of Sofia

COURSE STATUS IN THE CURRICULUM: Mandatory eligible for all students in the Automa-tion Information and Control Engineering MEng programme of the Faculty of Automation at the Technical University of Sofia. AIMS AND OBJECTIVES OF THE COURSE: The students acquire deep knowledge on the metrology assurance and metrology, as a foundation of the conformity assessment, as well as knowledge and orientation in the international and national quality providing structure. Besides the two base aspects of the metrology assurance (MA), providing the measurements traceability: the metrology inspection (verification) and the calibration, some organizational and normative prob-lems as metrology expertise, notification, type approval etc. are treated. DESCRIPTION OF THE COURSE: Subjects of study are the contemporary trends in the legal and normative formulations and requirements to the MA of the high-quality technologies, article and products. The organization and the activities in the MA, both on the national and working lev-els are presented. A special attention is paid to the measurement tools (MT) treatment activities, with an accent to practical applications of the working tools and the etalons. Methods, procedures, calibration and verification specifics are presented. The essence and the forms of the inspection of the measurement tools are overviewed: type approval, different verifications, metrology expertise and notification of MT. The range and the activities of the metrology surveillance are discussed. The knowledge from BEng course for: results treatment, presentation of the results, assessment and assuming of the errors and modeling, budgeting and assessment of the uncertainty; the methods and the measurement tools with its inherent influences over the measurement process; the Metrology Hierarchy of measuring instruments; estimating and standardizing of metrological characteristics of measuring instruments are interpreted in the aspects of the legal metrology and the activities in the non-regulated area nationally and internationally. PREREQUISITES: Mathematics, Physics, Electrical Measurements, Measurement of Non-Electrical Values, Quality Management and Control, Metrology inspection and calibration of meas-uring instruments TEACHING METHODS: Lectures, using slides, laboratory works with final reports, made by the students and revised by the teacher; Tasks and tests for current control; Personal course project. METHOD OF ASSESSMENT: Current control rating includes: current theory tests – 2 separate tasks by 20%, 40% total, Laboratory tasks assessment 20% and the personal course project 40% INSTRUCTION LANGUAGE: Bulgarian. BIBLIOGRAPHY: 1. Под ред. на Радев Х. Метрология и измервателна техника, Том 1, Софттрейд, София, 2010; 2. Радев Х., В. Богев. Неопределеност на резултата от измерването. С., Софтрейд, 2001; 3. Чаушев П. Метрология. С., ТУ-София, 1996; 4. Колев Н., П. Чаушев, В. Гавраилов. Основи на метрологичното осигуря-ване. С., Техника, 1982; 5. Euramet, July 2008, Metrology – in Short, 3rd Edition; 6. EAL Publication reference EA-4/02. Expression of Uncertainty of Measurement in Calibration (including supplement 1 to EA-4/02); 7. JCGM 200:2012 International vocabulary of metrology – Basic and general concepts and associated terms (VIM) 3rd edition 2008 version with minor corrections; 8. INTERNATIONAL OIML R 34 RECOMMENDATION Edition 1979 (E) Accuracy classes of measuring instruments; 9. GUIDANCE SERIES ILAC-G24 Edition 2007 (E) INTERNATIONAL OIML D 10 DOCUMENT Edition 2007 (E) Guidelines for the determination of calibration intervals of measuring instruments; 10. Съюз на метролозите в България, ФНТС, Бюлетин в помощ на специа-листа. Книжка1/2010, 1/2009, 3 и 4/2006, 3/2002, 7/2001, 6/2000.


DESCRIPTION OF THE COURSE Name of the course Robots in medicine





LECTURER:

Assoc. Prof. Ph.D. V.Zamanov (FA) – tel.: 965 2738, email: [email protected] Technical University of Sofia

COURSE STATUS IN THE CURRICULUM: Оptional discipline for the students in specialty “Robotics”, M Eng, program of the Faculty of Automation AIMS AND OBJECTIVES OF THE COURSE: To acquire the knowledge for the use of robots in the medicine and to also to point out the relations between modern robotics and human bio-mechanics.

DESCRIPTION OF THE COURSE: The main topics concern: robotic systems applied in surgery, orthopedics, microsurgery, cardio surgery. Analyze of typical application of robots for taking care of patients and in rehabilitation of lower and upper limb. Inside are studied robots used for transport, cleaning and control in the hospitals. Courses give knowledge about human bio-mechanics, for its modeling and experimental research. There is made structural analysis of endo-prostheis, exoskeletons and artificial arms and legs. In the courses are discussed some technical analogues, hoping of human, biological hyper redundant robots (snake like). Laboratory work gives students knowledge about some already developed projects of robots in medicine (ROBODOK, MINERVA) and bio-mechanical models and devices. PREREQUISITES: Mechanics, Robotic manipulators. Mobile robots. Mechatronic and machines in automatics

TEACHING METHODS: Lectures, using slides, case studies, laboratory and course work, work in teams, protocols. METHOD OF ASSESSMENT Two one-hour assessments at mid and end of semester (30+40%), laboratories (30%).


BIBLIOGRAPHY: 1. Zamanov V., Karastoianov D., Sotirov. Z. , Mechanics and control of robots, Sofia,1993; 2. Craig J. J., Introduction to Robotics: Mechanics and Control (3rd Edition), Prentice Hall, NJ, 2004;/; 3. Hirose, S., Snake - like Locomotors and Manipulators, Oxford Univ. Press, 1993; 4. Mobile Robots - State of the Art in Land, Sea, Air, and Collaborative Missions, Edited by X.Q. Chen, Y.Q. Chen, and J.G. Chase, In-Teh, Viena, Austria, 2009. p.346; 5. Raibert, M., K., Experiments in Balance with a 3D One Lagged Hopping Machine, The Int.Jourmnal of Robotics Research, Vol. 3, No 2, 1984.



DESCRIPTION OF THE COURSE Name of the course Digital Signal Processing





LECTURER:

Ass. Prof. Ph.D. Georgi Ruzhekov (FA) – tel.: 965 24 70, email: [email protected] Technical University of Sofia

COURSE STATUS IN THE CURRICULUM: Optional for the students specialty Automation, Information and Control Engineering, MEng programme, Faculty of Automation. AIMS AND OBJECTIVES OF THE COURSE: The main aim of the course is to get students familiar with the base and some of modern methods and applications of digital signal processing. DESCRIPTION OF THE COURSE: Described are the mathematical base of the digital signal processing, the analog to digital and digital to analog conversion process, the digital filters, decimation and interpolation. Discussed are some applications: data transmission, radiolocation, voice recognition, digital filter implementation. Laboratory works are based on Matlab and Simulink models.

PREREQUISITES: Physics, Mathematics, Control Theory, Electrical Engineering Theory, Semiconductor Elements, Programming and Computer Application. TEACHING METHODS: Lectures, using slides, laboratory and course work, work in teams, protocols and course work description preparation and defence. METHOD OF ASSESSMENT: Written final examination, including problems (60%); laboratory work (40%). INSTRUCTION LANGUAGE: Bulgarian. BIBLIOGRAPHY: 1. Ruzhekov G., Signal and Data processing, TU-Sofia, 2011. 2. Ruzhekov G., Signal and Data Processing Laboratory Exercises, TU-Sofia, 2009. 3. Ivanov R., Digital processing of the one-dimensional signals, Gabrovo, 2002., 4. Ifeachor E, B. Jerrvils, Digital Signal Processing – A practical Approach, Addison-Wesley Publishing Company 1993.


DESCRIPTION OF THE COURSE Name of the course Fractional Control





LECTURER: Prof. D.Sc. E. Nikolov (FA) – tel.: 965 3417, еmail: [email protected] Technical University of Sofia

COURSE STATUS IN THE CURRICULUM: Compulsory for the students specialty Automa-tion, Control and Information Instrumentation programme of the Faculty of Automation “master”

AIMS AND OBJECTIVES OF THE COURSE: At the end of the course students are expected to be able to apply the methodology and expertise in the application of fractal operators for integration and differentiation and their approximations in algorithms and control systems

DESCRIPTION OF THE COURSE: Below are the theoretical foundations of integral transfor-mations, generalized fractional calculus and their applications in industrial control systems

PREREQUISITES: Control Theory, Control Instrumentation, Industrial Automation, Logic Con-trol, Multivariable Systems, Application Method Control.

TEACHING METHODS: Lectures, using slides, case studies, laboratory and course work, work in teams, protocols and course work description preparation and defense. METHOD OF ASSESSMENT: Two one-hour assessments at mid and end of semester (62%), la-boratories (18%), course work - two off assignments (20%)


BIBLIOGRAPHY: Николов Е. (2010), Робастно Фрактално Управление (предиктивни и алгебрични мето-ди; системи с разпределени параметри), София 2010, 2010 Изд. Технически Университет София, ISBN -978-954-438-851-5, 375 стр.; Николов Е. (2004), Фрактални алгоритми и режекторни рагулатори, София 2004, 2004 Изд. На ТУ-София, ISBN 954-438-395-6, 2004, 216 стр.; Николов Е. (2004), Специални математически функции и фрактални оператори (справочно пособие), София 2004, 2004 Изд. на Технически Университет София, София, ISBN 954-438-423-5, 2004, 108 с.; Николов Е. (2005), Робастни системи (приложни методи за управление натехнологични процеси - II част), София 2005, 2005 Изд. на Технически Университет София, 2005, ISBN 954-438-499-5, 144 p.; Николов Е., Д. Жоли, Н. Николова, Б. Бенова (2005), Commande Robuste, Sofia 2005, 2005 Ed de l’Université Technique de Sofia, 2005, ISBN 954-438-500-2, 216 p.; Николов Е. (2003), Приложни методи за управление на процеси - I част (честотни методи и системи с робастни свойства), Изд. на ТУ-София, ISBN 954-438-334-4, 2003, 358 стр.; Николова Н., E. Николов (2009), Приложни методи за уп-равление на технологични процеси, ръководство за лабораторни упражнения, София 2009, Изд. Технически Университет София, ISBN 978 954 438 784 6, 120 стр.; Николова Н., E. Николов (2006), Методи и алгоритми за настройка на регулатори в системи за управление - Справочно пособие по дисциплината Приложни Методи за Управление на Технологични Процеси, София 2006, Изд. Технически Университет София, ISBN –10: 954 438 579 7; ISBN –13: 978 954 438 579 8, 72 стр.; Oustaloup A. (1991), La commande CRONE (commande robuste d’ordre non entier), Hermes (Traité des Nouvelles Technologies - Sйrie Automatique), Paris, 495 p.; Oustaloup A. (1996), La dérivation non entiere (théorie, synthése et applications), Hermes (Traité des Nouvelles Technologies - Série Automatique), Paris, 508 p.


DESCRIPTION OF THE COURSE Name of the course Production automation systems



Lessons per week: L – 2 hours; Lab. – 2 hours


LECTURER: Assoc. Prof. Ph.D. Todor Ionkov (FA) – tel.: 965 29 50, e-mail: [email protected]


COURSE STATUS IN THE CURRICULUM: Common for the students specialty Automation, Information and Control Engineering, MEng programme, Faculty of Automation.

AIMS AND OBJECTIVES OF THE COURSE: Based on the main technology production schemes, the course analyses the production systems by formalizing them to organization—structure models and control algorithms.

DESCRIPTION OF THE COURSE: Many multi-mass and multi-linked production systems (consecutive, parallel, chained) as well as production systems with interrupted and discretely uninterrupted character are subject of examination. Production systems with variable parameters are analyzed, and a special interest is focused on the restrictions of their phase coordinates as well as non-linear multi-linked production systems. The attention is concentrated upon optimum automated production systems (optimum parameters as to fast operation, losses of energy, energy consumption, maximum punctuality). The problems of parametric optimization of the production systems are explored, as well as the ones concerning optimal static corrections in production systems from uninterrupted chained type. The contemporary devices for real time identification and for realization of adaptive strategy of production system’s drive.

PREREQUISITES: Theory of Automation Control, Electromechanical systems, Control of Electromechanical systems, Advanced control theory

TEACHING METHODS: Lectures.

METHOD OF ASSESSMENT: Written exam in the end of course. INSTRUCTION LANGUAGE: Bulgarian BIBLIOGRAPHY: 1. WEINMANN ALEXANDER, Regelungen - Analyse und technischer Entwurf , Band 2,

Multivariable, igitale und nichtlineare Regelungen, optimale und robuste Systeme, Springer-Verlag, Wien.

2. БОРЦОВ Ю.А. и др. Электромеханические системы с адаптивным и модальным управлением, Л., Энергоатомиздат, 1984.

3. ЕГОРОВ В.Н., О.В.Корьнежевский, Цифровое моделирование систем электропривода, Л., Энергоатомиздат, 1986.

4. ЦЫКУНОВ М., Адаптивное управление обьектами с последствием, М., Наука, 1984.


DESCRIPTION OF THE COURSE Name of the course Intelligent Measurement Systems





LECTURER: Assist. Prof. Ph.D. Nikolay Stoyanov (FA) – tel.: 9653463, email: [email protected]


COURSE STATUS IN THE CURRICULUM: Compulsory course for the students specialty Automation, Information and Control Engineering, MEng programme, Faculty of Automation.

AIMS AND OBJECTIVES OF THE COURSE: The course includes special features in building of intelligent measurement systems and application of systems with artificial intelligence to manage of the measurement process. The main attention has been spared on the more important intelligent functions in measurement systems.

DESCRIPTION OF THE COURSE: The main topics concern: Microprocessor information and measurement systems; Telemetric measurement systems, Intelligent functions in measurement systems; Methods for mathematical modelling of the measurement process; Intelligent sensors and sensor network; Remote calibration of measurement systems; Virtual systems for measurements; Artificial neural network for ménage of the measurement process; Expert systems in the measurements. PREREQUISITES: The discipline is built on the basis of students’ knowledge obtained in the following courses: Electrical measurements, Intelligent Measurement Instrumentation, Information and Measurement Systems. TEACHING METHODS: Lectures, supported with slides. Laboratory work with special models, reports and defence.. METHOD OF ASSESSMENT: Exam in the end of semester INSTRUCTION LANGUAGE: Bulgarian BIBLIOGRAPHY: 1. Калчев И., (2006), Интелигентни измервателни системи. Технически Университет – София; 2. Bhuyan M., (2011), Inteligent Instrumentation, Principles and Application, CRC Press; 3. Нишева М., Шишков П., (1995), Изкуствен интелект, Издателство ”Интеграл”, Добрич; 4. Nakra B. C.,Chaudhry K. K., (2004), Instrumentation, Measurements and Analysis, second edition, Tata McGraw – Hill; 5. Morris A., (2001), Measurements and Instrumentation Principles, third edition, Butterworth-Heinemann; 6. Стоянов И., (2000), Измервания в електрониката, Технически Университет – София

DESCRIPTION OF THE COURSE Name of the course Fuzzy Control and Neural Networks



Lessons per week: L – 2 hours; Lab. – 2 hours


LECTURER: Prof. Ph.D. V. Mladenov (FA) – tel.: 965 2386, e-mail: [email protected]

Prof. Ph.D. S. Yordanova (FA) – tel.: 965 3313, e-mail: [email protected] Technical University of Sofia

COURSE STATUS IN THE CURRICULUM: Compulsory for the students specialty Automation, Information and Control Engineering, MEng programme, Faculty of Automation. AIMS AND OBJECTIVES OF THE COURSE: To provide basic knowledge for fuzzy sets theory and fuzzy logic as well as for the main types of artificial neural networks (ANN) and their training algorithms for modelling, control and optimisation of complex systems. DESCRIPTION OF THE COURSE: Main topics: Fuzzy sets, relations and logic; Membership functions; Types of composition rules; Linguistic variables; Fuzzy input-output relationship mapping; Fuzzy inference mechanisms; Fuzzy modelling; Mamdani, Larsen and Takagi-Sugeno models; Fuzzy and Neuro-Fuzzy controllers; Control strategy and surface; Fuzzy control systems – tuning, stability, robustness; Artificial Neural Networks (ANNs) - biological prototype, architectures; Single layer neural networks (NNs) and training methods (Hebb’s rule, delta rule); Multi-layer NNs – backpropagation of error training method; Radial basis functions NNs; Self-organizing, recurrent, probabilistic and dynamic NNs; Neuro-fuzzy modeling and control; Software tools Fuzzy Logic Neural Networks and SIMULINK toolboxes of MATLAB; Case studies and applications from power plants, ecology and communications. PREREQUISITES: Math, Electrical Engineering, Control Theory, Systemс Identification, Computer Simulation, Process Control, Systems Optimisation, Adaptive Control. TEACHING METHODS: Lectures using slides and PowerPoint presentations, laboratory work in computer class from laboratory manual, based on SIMULINK, Neural Networks, and Fuzzy Logic Toolboxes of MATLAB. METHOD OF ASSESSMENT: A two-hour exam in two parts - Fuzzy Control (40%) and ANN (40%) plus laboratories (20%). INSTRUCTION LANGUAGE: Bulgarian. BIBLIOGRAPHY: 1. Младенов В. и С. Йорданова, Размито управление и невронни мрежи, ТУ-София, С., 2006, 168, ISBN 978-954-438-595-8; 2. Йорданова С., В. Младенов, Г. Ценов, Р. Цекова, Размито управление и невронни мрежи. Ръководство за лабораторни упражнения, ТУ-София, С., 2008, 121, ISBN 978-954-438-720-4; 3. Йорданова С., Методи за синтез на размити регулатори за робастно управление на процеси, КИНГ, С., 2011, 344, ISBN 987-954-9518-68-91; 4. Driankov D., H. Hellendorn, M. Reinfrank, An Introduction to Fuzzy Control, Springer Verlag, 1993; 5. Fausett L., Fundamentals of Neural Networks, Prentice-Hall, 1994, ISBN 0130422509; 6. Ham F., Kostanic I., Principles of Neurocomputing for Science and Engineering, McGraw-Hill, 2001, ISBN 007118161X; 7. Haykin S., Neural Networks: A comprehensive foundation, 2nd Edition, Prentice Hall, 1999, ISBN 0132733501; 8 Jantzen J., Foundations of Fuzzy Control, John Wiley & Sons Inc., 2007; 9. Kosko B., Neural Networks and Fuzzy Systems, Prentice Hall, USA, 1992; 10. Ross T.J., Fuzzy Logic with Engineering Applications, McGraw Hill, Inc., 1995; 11. Yager R. R. and D. P. Filev, Essentials of Fuzzy Modelling and Control, John Wiley & Sons, Inc., N.Y., 1994, ISBN 0-471-01761-2.

List 4 1 Engineering methods in electro mechanics MAICE11.1 2 Design of Control Systems for the Biotechnological Production MAICE11.2 3 Man-machine Control Systems MAICE11.3 4 Measurement and Control of the Parameters of the Environment MAICE11.4 5 Intelligent Behaviour Systems MAICE11.5 6 Digital Estimation and Control MAICE11.6


Name of the course Engineering methods in electro mechanics





LECTURER: Assoc. Prof. Ph.D. Rumen Rainov (FA) – tel.: 965 39 45, e-mail: [email protected]


COURSE STATUS IN THE CURRICULUM: Optional for the students specialty Automation, Information and Control Engineering, MEng programme, Faculty of Automation. AIMS AND OBJECTIVES OF THE COURSE: The aim of the course is to give the students knowledge about specific methods of solving engineering tasks in electromechanics area. Experi-ence of computer based problem solving will be acquired.

DESCRIPTION OF THE COURSE: The course gives knowledge about methods for drive pa-rameters detection, working machine and working regimes; mechanical and electromechanical characteristics at special brake regimes of asynchronous drives; transient processes in electric drives; power calculations in electric drives.

PREREQUISITES: Physics, Technical Mechanics, Electronics, Electromechanical devices, Auto-mation of manufacturing machines, Control Theory.

TEACHING METHODS: Lectures assisted with slides on multimedia, Laboratory works on phys-ical and computerized models. Written materials on the lectures and laboratory works are given to students.

METHOD OF ASSESSMENT: Continuous assessment. INSTRUCTION LANGUAGE: Bulgarian BIBLIOGRAPHY: 1. Йорданов С., Р.Райнов, Д.Крайчев, Избор на оптимални параметри на реверсивни

електрозадвижвания, “Техника”, 1980, с.202. 2. Ключев В., Теория на електрозадвижването, “Техника”, 1989, с.543. 3. Райнов Р., Особености на механичните характеристики на асинхронен двигател в режим

на кондензаторно спиране, сп. “Електроника и електротехника”, кн.3-4, 1996г., с.37-40. 4. Райнов Р., Христов Вл., Формиране на механичните характеристики на асинхронен

двигател в режим на кондензаторно спиране, 2004 – Международна юбилейна научна сесия 30 години факултет "Автоматика", ТУ – София,

5. Йорданов С., Р.Райнов, Изчисляване на характеристиките и оразмеряване на елементите на схемата при режим на динамично спиране със самовъзбуждане от изправения роторен ток, сп. “Електропромишленост и приборостроене”, кн.1, 1982, с.14-17.


DESCRIPTION OF THE COURSE Name of the course: Design of Control Systems for the Biotechnological Production




Number of credits:5

LECTURER:

Stoyan Nedelchev Stoyanov, Ph.D., Associate Professor, Faculty of Automation Tel.: 840-30-45, e-mail: [email protected]


COURSE STATUS IN THE CURRICULUM: Compulsory for the students specialty “Automa-tion, Information and Control Engineering ” , “Faculty of Automation”, Sofia Technical Universi-ty, education-qualification degree “Magister”.

AIMS AND OBJECTIVES OF THE COURSE: Students acquire knowledge about automatic / ednokonturni, cascading, multicontour / hierarchical control systems and modern management systems, algorithmic their security, reliability, feasibility study of decisions and their impact on the environment.

DESCRIPTION OF THE COURSE: Topics to be covered:

Methods for designing automatic control systems. Issues related to the characteristics of modern management systems, algorithmic their security, reliability, feasibility study of decisions and their impact on the environment. Particular attention is paid to measures to protect the information chan-nels. Students are familiarized with modern technical means and approaches to creating a stock of information. Students learn the elements of engineering graphics. Learn practical skills to work with software for the automation of technical design of control systems in biotechnology. Laborato-ry exercises allow you to gain practical skills in setting different control algorithms, as well as for configuring real control systems for biotech industries. Students learn about specific programs for design and engineering graphics.

PREREQUISITES: Control Theory, Basic of the Bioelectroengineering, Technical Elements of Industrial Automation, Measurement of Nonelectrical Variables, Fermentation of the Technologies, Bioatomation, Design of the Automation System for Biotechnical Processes, Control System of the Biotechnical Production and Automation System in Biotechnolidies.

TEACHING METHODS: Lectures, Power Point slides, case studies, laboratory, work in teams, protocols, examinations with two questions.

METHOD OF ASSESSMENT: Two one-hour exams at mid and end of the semesters (75% of the course grade), laboratories (25% of the course grade).


BIBLIOGRAPHY: 1. M. Hadjiski., (1992), Design Control System of Technical Processes I, S., Technika. 2.I.Dikov, (1984), Design Control System of Technical Processes II, S., Technika. 3.M. Hadjiski., (1992), Automation of Technical Processes, Sofia. 4. St.Tsonkov, D.Filev, I.Simeonov, L.Vaklev, (1992), Control of Biotechnical Processes, S., Technika. 5. Birukov V., V. M. Kantare (1985) Optimisation of the batch fermentation processes, Mockow, Nauka. 6. Popovic D and V.P. Bhatkar, (1990), Distributed Computer Control for Industrial Automation, Marcel Dekker, Inc. 7. I.Tomov., (1986), Microprocess Control, Technika, Sofia.


Name of the course:

Man-machine Control Systems

Code:MAICE11.3 Semester: 2




LECTURER:

Assoc. Prof. Ph.D. V. Galabov (FA) – tel.965 22 98, email: [email protected] Technical University of Sofia

COURSE STATUS IN THE CURRICULUM: Optional for the student's specialty Automation, Information and Control Engineering, Masters Degree programme, Faculty of Automation.

AIMS AND OBJECTIVES OF THE COURSE: To present human factor problems in automated systems.

DESCRIPTION OF THE COURSE: The key psycho-physiological characteristics of man’s receptor and effector channels are analyzed, as well as how the respective information processes take place. Students are to learn about various problems related to training and self-training, fatigue and compensatory mechanisms, stereotypes and creative behaviour. Special emphasis is placed on various strategies and methods for man-machine problem solutions, delegation of managerial functions, design and implementation of respective interface systems.

PREREQUISITES: Computer simulation, Industrial Information Systems, Decision Making in Control Systems, Bioelectricalengineering Fundamentals, Biotechnological Measurements.

TEACHING METHODS: Lectures, using slides, case studies, laboratory work from laboratory manual, work in teams, protocols preparation and defence.

METHOD OF ASSESSMENT: Two one-hour assessments at mid and end of semester (total 72% - 40% problem + 32% theory) plus laboratories (total 28% - 14 assignments, each carrying 2%)


BIBLIOGRAPHY: 1. Гълъбов, В. (2010), Човеко-машинни системи за управление. Ч. 1., Изд. на Технически Университет София, 2010; 2. Морган Т. К. и др. (1971), Инженерная психология в применении к проектированию оборудования., Машиностроение, М., 1971 (ориг. Morgan T. Cl. Human Engineering Guide to Equipment Design., Mc. Craw-Hill Book Company, Inc., New York, Toronto, London); 3. Венда В. (1982), Инженерная психология и синтез систем отображения информации., Машиностроение, М., 1982; 4. Даскалов И., И. Стамболиев (1987), Електро-медицинска диагностична техника., Техника, С., 1987; 5. Ломов Б. (1990), Человек и техника, ЛОЛГУ, Ленинград, 1990.

DESCRIPTION OF THE COURSE Name of the course Measurement and Control of the Parameters of the Environment





LECTURER: Assist. Prof. Ph.D. A. Pandelova (FA) – tel.: 965 3463, email: [email protected]


COURSE STATUS IN THE CURRICULUM: Elective course for the students specialty Automatics, Information and Control Technics, МEng programme of Faculty of Automatics

AIMS AND OBJECTIVES OF THE COURSE: To familiarize the students with fundamental problems of ecology, the main international agreements and standards for monitoring and protecting the environment. Special attention is paid to the methods of control. DESCRIPTION OF THE COURSE: The main topics concern: pollution of the atmosphere, water and soil, measured and controlled parameters; legal base for eco-monitoring, methods and equipment for monitoring the condition of the atmospheric air, surface and groundwater, soil and vegetation environmental monitoring; metrological assurance system for eco-monitoring. PREREQUISITES: Electrical Engineering, Electrical Measurements, Measurement of non-electrical quantities, Analytical Measurements, Metrology and Quality Assurance TEACHING METHODS: Lectures, using slides, laboratory and course work, work in teams, protocols and course work description preparation and defence. METHOD OF ASSESSMENT: Two one-hour assessments at mid and end of semester, laboratories, course work. INSTRUCTION LANGUAGE: Bulgarian. BIBLIOGRAPHY: 1. Г.Близнаков, И. Митов. Въведение в химичните проблеми на околната среда и в екологичното право,стандартизация и мониторинг. Академично издателство „Проф. Марин Дринов”, София, 2001. 2. Б. Захаринов, Я. Найденов. Екологичен мониторинг, НБУ, София, 2007. 3. R. Kellner, J.-M. Mermet, M. Otto, H. Widmar. Analytical Chemistry. WILEY-VCH, 2002. 4. . Г. Крисчън, Дж. О’Райли, под редакцията на чл. кор. Дхн П. Бончев. Инструментален анализ, университетско издателство „Св. Кл. Охридски”, 2003. 5. А. Нейков. Биосензорни системи и анализатори. Технически Университет – София, 1996. 6. 3. Харитонов, Ю. Я. Аналитическая химия, Москва, 2003. 7. . Environmental standards. ISO library. 8. Нейков А., Костов Й. Ръководство за лабораторни упражнения по аналитични методи и уреди”. ТУ-София, 1999.

DESCRIPTION OF THE COURSE Name of the course Intelligent Behaviour Systems



Lessons per week: L – 2 hours; Lab. – 1 hour


LECTURER:

Assoc. Prof. Ph.D. Dimitar Dimitrov (FA), tel. 965 2636, email: [email protected], Technical University of Sofia

COURSE STATUS IN THE CURRICULUM: Optional for the students specialty Automation, Information and Control Engineering, Robotics MSc programme, Faculty of Automation.

AIMS AND OBJECTIVES OF THE COURSE: Obtaining knowledge about a core of relatively new developing algorithms and modern architectures, wich have great practical importance for the designing of intelligent behavior systems (IBS). Getting skills in research and development activities. DESCRIPTION OF THE COURSE: The main topics include basic IBS architectures, meta-heuristics for solving NP-hard problems – simulating annealing, real time search, genetic and ants algorithms. Fuzzy logic implementation for selection, arbitration and fusion of atomic behaviors, classic and emergent functionality behavior architectures of mobile robots. Usage of multi valued logics and probability network for the fusion of reactive and deliberative behaviors. Methods for building learning agents - inductive concept learning by examples and neural network. The course provides variety of practical illustrations – navigation of mobile robots, automatic generation of decision support strategies, nonlinear optimization ion, rooting and transportation problems etc. Inputs: Artificial Intelligence. Outputs: All special disciplines and diploma project.

PREREQUISITES: Robotics, Logic Modeling and Programming

TEACHING METHODS: Lectures using slides, computer programs handouts, laboratory protocols preparation and defence, work in teams, discussions. Licensed program packages are used for laboratory work and for designing didactically proper IBS modules. METHOD OF ASSESSMENT: Continuous assessment, including written tests (60%), laboratories (15%), course work and discussions (25%).

INSTRUCTION LANGUAGE: Bulgarian BIBLIOGRAPHY: 1. Димитров, Системи с интелигентно поведение. ISBN 954-438-457-X. ТУ-София, 2005. 2. Димитров, Д.Н. Никовски. Изкуствен интелект – второ преработено издание. ISBN 954-438-252-6. ТУ - София. 3. Russel. P. Norvig. Artificial Intelligence. Prentice Hall, 2003. 4. Latombe, J. C. Robot Motion Planning. Kluwer, Dordrecht, The Netherlands, 1991. 5. B. Kosko. Neural Network and Fuzzy Systems. Prentice-Hall Int. Inc., 1992. 6. Zb. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs, Springer Verlag, 1992.


DESCRIPTION OF THE COURSE Name of the course Digital Estimation and Control





LECTURER: Prof. eng. Emil Mihaylov Garipov, PhD (FА), тел.: 965 3459, еmail: [email protected],

Assoc. prof. д-р eng. Tsonyo Nikolaev Slavov (FА), тел.: 965 2420, email: [email protected]

COURSE STATUS IN THE CURRICULUM: Compulsory for the students specialty System and Control Engineering, MEng programme of the Faculty of Automatics.

AIMS AND OBJECTIVES OF THE COURSE: The course has two aims. First, the basic elements of the estimation theory to be studied about parameters and states of the stochastic discrete-time (linear and non-linear, time-invariant and time-variant) processes. Second, this knowledge to be used in the adaptive control tasks as joint functioning area of the model estimation during plant uncertainty and control signal design during the control system uncertainty.

DESCRIPTION OF THE COURSE: The various schemes for random signals generation and different approaches of the continuous signals and systems discretization are studied. Some knowledge and skills of the stochastic approach to the block and recursive parameter estimation via different performance indexes are remembered. Methods for time-variant stochastic processes estimation in open- and closed-loop are presented. There is a natural transfer to the state estimation by Kalman filters type for prediction, filtering and smoothing. A lot of examples are given to realize adaptive control systems (gain scheduling, self-tuning controllers, multiple-model adaptive control).

PREREQUISITES: Control Theory, System Identification, Digital Control Systems, Digital Signal Processing.

TEACHING METHODS: Lectures using PowerPoint presentation, laboratory exercises with protocols and course project description preparation and defence. METHOD OF ASSESSMENT: Laboratory exercises protocols defence (80%), two homeworks at mid and end of semester (20%), course project defence independent mark (100%).

INSTRUCTION LANGUAGE: Bulgarian BIBLIOGRAPHY: 1. Garipov, Е., Ts. Slavov (2012). Digital Estimation and Control (lecture note in e-format) (in Bulg.). 2. Garipov, Е., Ts. Slavov (2012). Instructions for Laboratory Exercises on Digital Estimation and Control ((in e-format) (in Bulg.). 3. Garipov, Е., Ts. Slavov (2009). Manual for Laboratory Exercises on System Identification. TU-Sofia (in Bulg.). 4. Garipov, Е. (1997, 1999). Solved problems for Control System Design in MATLAB/SIMULINK. TU-Sofia (in Bulg.).

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List 5 1 Impulse semiconductor converters with vector control MAICE12.1 2 Multivariable Automation Systems MAICE12.2 3 Design of control systems with guaranteed performance MAICE12.3 4 Processing and Analysis of Measurement Information MAICE12.4 5 Diagnosis and Testable Design of Robot Systems MAICE12.5 6 Optimization and Decision Making MAICE12.6


Name of the course Impulse semiconductor converters with vector control





LECTURER:

Assoc. Prof. Ph.D. Todor Ionkov (FA) – tel.: 965 29 50, e-mail: [email protected]


COURSE STATUS IN THE CURRICULUM: Optional for the students specialty Automation, Information and Control Engineering, MEng programme, Faculty of Automation.

AIMS AND OBJECTIVES OF THE COURSE: The aim of the course is to give the students knowledge in research and development work in the area of power semiconductor converters.

DESCRIPTION OF THE COURSE: Attention is paid to the vector control of power semicon-ductor converters about uninterruptible power supplies, electric drives with synchronous, asynchro-nous and permanent magnet drives in industry and electric transport. Laboratory works are illustrat-ed with real power semiconductor systems.

PREREQUISITES: Electromechanical devices, Power electronics in electric drives, Control of Electromechanical Systems, Control Systems of Electric Drives.

TEACHING METHODS: Lectures and Laboratory works.

METHOD OF ASSESSMENT: Written exam in the end of the course. INSTRUCTION LANGUAGE: Bulgarian BIBLIOGRAPHY: 1. ШРЕЙНЕР Р.Т. Ю.А.Димитренко, Оптимальное честотное управление асинхронными

электроприводами, "Штиница", Кишинев, 1982. 2. VAS P. Senzorless Vector and Direct Torque Control, Oxford University Press, 1998. 3. ЭПШТЕЙН И.И., Автоматизированный электропривод переменного тока, Энергоиздат, М.1982. 4. РУДАКОВ В.В. и др. Асинхронные электроприводы в векторным управлением, Энергоатомиздат,

Л.1987. 5. BENJAMIN C.KUO, Automatic control sistems, 1993. 6. ТЕРЕХОВ В.М., Элементны автоматизированного електропривода, Энергоатомиздат, М., 1987. 7. BLASCHKE F. Das Prinzip der Feldorientierung die Grundlage fur die TRANSVECTOR - Regeiung

won Drehfeldmachinen Siemens - Zq 1971, Bd 45, № 10.



Name of the course Multivariable Automation Systems





LECTURER: Ass. Prof. Alexander Efremov (FA), тел.: 0896861315,

еmail: [email protected], url: http://anp.tu-sofia.bg/aefremov/index.htm Technical University – Sofia

COURSE STATUS IN THE CURRICULUM: Optional for the students specializing in Automation, Information and Control Engineering, MEng programme, Faculty of Automation AIMS AND OBJECTIVES OF THE COURSE: The aim is to give students insight into nonlinear multivariable system identification and its application in techniques, economics, finance, sociology, etc.

DESCRIPTION OF THE COURSE: The course provides an overview of nonlinear multivariable system identification. A strong attention is paid on the optimization methods (unconstrained and constrained), the possible numerical problems and their stable solutions. Stepwise procedures are represented for (multivariable and nonlinear) model structure determination. Real-life examples of automatic system identification are also considered.

PREREQUISITES: Mathematical analysis, Linear Algebra, Statistics, System Identification, Modelling and Process Optimization, Signal processing, Control Theory

TEACHING METHODS: Lectures, laboratory and course work with real datasets. METHOD OF ASSESSMENT: Exam

INSTRUCTION LANGUAGE: Bulgarian BIBLIOGRAPHY: 1. Е. Гарипов, (2004), Идентификация на системи. Част I. Въведение. Технически Университет, София, второ издание, ISBN 954-438-391-3; 2. Е. Гарипов, (2004), Идентификация на системи. Част II. Идентификация чрез дискретни стохастични регресионни модели. Технически Университет, София, второ издание, 2004, ISBN 954-438-392-1; 3. И. Вучков, (1996), Идентификация, ИК Юрапел, София; 4. D. V. Hosmer, S. Lemeshow, (2000), Applied Logistic Regression, John Willey & Sons, Inc., Canada, ISBN: 0-471-35632-8.; 5. J. Nocedal, S. J. Wright, (2006), Numerical Optimization, Springer Series in Operations Research and Financial Engineering. Springer Science + Business Media, LLC, USA, ISBN-10: 0-378-30303-0. 6. O. Nelles, (2001), Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models. Springer-Verlag, NewYork, ISBN 3-540-67369-5. 7. J. M. Zurada, (1992), Introduction to Artificial Neural Systems. West Publishing Company, ISBN: 0-314-93391-3.


http://anp.tu-sofia.bg/aefremov/index.htm�


Name of the course Design of control systems with guaranteed performance

Code: МAICE12.3 Semester: 2




LECTURERS: Assoc. Prof. Nina Nikolova, PhD, tel. 965 34-89, 965 25-57; [email protected]

Assist. Prof. Vessela Karlova-Sergieva, PhD, tel.965 69 41; [email protected] Technical University – Sofia

COURSE STATUS IN THE CURRICULUM: Optional for the students specializing in Automation, Information and Control Engineering, MEng programme, Faculty of Automation.

AIMS AND OBJECTIVES OF THE COURSE: At the end of the course the students are expected to: apply theoretical knowledge for synthesis of control systems in the frequency domain and complex plane in objects with uncertainty in their parameters; have practical knowledge for control of different types of automatic continuous and discrete systems with guaranteed quality, the domains of their application, characteristics, advantages and disadvantages

DESCRIPTION OF THE COURSE: The main topics concern: Engineering approach to control systems design; Performance of control systems; Robustness of control systems; Parametric uncertainty types; Two degree of freedom control; The place of quantitative feedback theory in the domain of robust control; Design of control systems with guaranteed performance; Discretization in control systems; etc.

PREREQUISITES: Control Theory, Elements of Industrial Automation, Applied methods for process control, Control Engineering

TEACHING METHODS: Lectures, using multimedia slides. The basic structure of the lecture is projected, some definitions and the most essential knowledge, variables, sketches, relations, graphics and formulas. During the laboratory exercises concrete tasks are solved with the help of computer or lab

oratory stand. This way the students learn the matereal better, apply the theoretical knowledge, and learn to take decisions of their own.

METHOD OF ASSESSMENT: The final mark is formed by the components: the results of the three tests - two ongoing evaluations in the middle and end of the semester (60%); laboratory exercises (20%); course work (20%).


BIBLIOGRAPHY: 1.Borghesani C., Y. Chait, O. Yaniv, The QFT Frequency Domain Control Design Toolbox, User’s Guide, 3rd ed, Terasoft, Inc., 2003. 2. Houpis C., S. Rasmussen, Quantitative Feedback Theory, Marcel Dekker Inc., 1999. 3. Garcia-Sanz M., Quantitative Robust Control Engineering: Theory and Applications. In Achieving Successful Robust Integrated Control System Designs for 21st Century Military Applications – Part II. Educational Notes RTO-EN-SCI-166, pp. 11-44, 2006. 4. Петков П., М. Константинов, Робастни системи за управление – Анализ и синтез с Matlab, ABC Техника, С., 2002.




Name of the course Processing and Analysis of Measurement Information





LECTURER: Prof. Ph.D. T. Tashev (FA) – tel.:, 029653465; e-mail: [email protected]

Technical University of Sofia COURSE STATUS IN THE CURRICULUM: Elective course for the students specialty Automatics, Information and Control Technics, МEng programme of Faculty of Automatics

AIMS AND OBJECTIVES OF THE COURSE: To familiarize students with the basic structures and algorithms for processing and analysis of test data obtained in modern automated equipment and systems. The main goal of this treatment is to reduce the impact of different disturbances on the measurement results, improving overall accuracy and increasing the reliability of the data. DESCRIPTION OF THE COURSE: The main topics concern: structural methods for improving the accuracy of measurement, presentation of complex signals by orthogonal decomposition of system functions, transformation of the signals in measurement systems, dynamic errors, discretization of the signal, error recovery signal, Z-transformation, filtration, approximation of graduation characteristic, algorithmic methods for improving the accuracy of measurement. PREREQUISITES: Theory of the signals, Introduction to the theory of management, Process Control Systems. TEACHING METHODS: Lectures, using slides, laboratory and course work, work in teams, protocols and course work description preparation and defence. METHOD OF ASSESSMENT: Two one-hour assessments at mid and end of semester, laboratories, course work. INSTRUCTION LANGUAGE: Bulgarian MAICE08.4 BIBLIOGRAPHY: 1. Steven W. Smith, Digital Signal Processing, California Technical Publishing, San Diego California, 1999. 2. Г. Ружеков, Обработка на данни и сигнали, Технически университет, София, 2011. 3. Р. Иванов, Цифрова обработка на едномерни сигнали, Габрово, 1997, 4. А. Опенхайм и др., Сигнали и системи, Техника, София, 1993, 5. Е. Гарипов, Решени задачи по проектиране на системи за управление в MATLAB и SIMULINK, Технически университет-София, 1997, 6. Г. Ружеков, Ръководство за лабораторни упражнения по обработка на данни и сигнали, Технически университет, София, 2009.


DESCRIPTION OF THE COURSE Name of the course Diagnosis and Testable Design of Robot Systems





LECTURER:

Assoc. Prof. Ph.D. Ivan Avramov (FA) – tel.+359 2 9653991, Email: [email protected] Technical University of Sofia

COURSE STATUS IN THE CURRICULUM: Eligible for the students specialty “Automatics, Information and Control Engineering” at the Master Eng. Program of the Faculty of Automation.

AIMS AND OBJECTIVES OF THE COURSE: To develop the basic knowledge in technical diagnosis and discuss the problems of the reliable, testable and fault-tolerant design in robotics on this basis.

DESCRIPTION OF THE COURSE: The course develops the topic of the fault-detection test creation in the field of synchronous and asynchronous type sequential circuits by the expansion of algorithms of Roth, Poage-McCluskey and the Critical-way algorithm. There are discussed some ways of test-creations, tolerant in hazards and critical risk as well as some applications of self-testing robot-systems. The list of classical fault definitions is being extended and special emphasis is laid on the reduction and minimization of fault-detection tests. The students are being familiarized with the application of multivalued logics for modeling of the faults as well as with the application of stochastic Petri Net and Artificial Neural Networks in this field. A considerable part of the course is being devoted to the reliable and fault-tolerant design of robot subsystems. There are also investigated the problems of analysis and evaluation of the reliability of robot-systems on the basis of static and dynamic models as well as on that of homogeneous Markov’s models. Attention is paid to some economic criteria of the reliability in robotics. There is a focus on the means of enhancing the reliability in the design process.

PREREQUISITES: Mathematics, Robotics, System Modeling, Design of Robot Systems.

TEACHING METHODS: Lectures, using available didactic means in TU, laboratory work.

METHOD OF ASSESSMENT: Two written tests.


BIBLIOGRAPHY: 1. Breuer M.A. and Friedman A.D.,Diagnosis and reliable design of digital systems, London, 1977; 2. Lala P.K., Fault tolerant and testable hardware design,Englewood Cliffs etc., Prentice-Hall, 1985; 3. Chakradhar S.T., V.D.Agrawal, M.L.Bushnell, Neural models and algorithms for digital testing, Kluwer Academic Pulishers, 1995; 4. Peterson J.L., Petri net theory and the modeling of systems, Englewood Cliffs, Prentice-Hall, N.J.,1981; 5. Zhou M. C., Frank DiCesare, Petri net synthesis for discrete event control of manufacturing systems, Englewood Cliffs, Prentice-Hall, 1991.


DESCRIPTION OF THE COURSE Name of the course Optimization and Decision Making





LECTURER: Assoc. Prof. Ph.D. T. Puleva (FA) – tel.: 965 2526, email: [email protected] Ass. Prof. Ph.D. A. Markovski (FA) – tel.: 965 2452, email: [email protected]

Technical University of Sofia COURSE STATUS IN THE CURRICULUM: Optional for the students specialty Automation, Information and Control Engineering, MEng Programme, Faculty of Automatics. AIMS AND OBJECTIVES OF THE COURSE: At the end of the course, the students will dispose of the methodology and will have knowledge for solving scientific and engineer problems, which include optimization subtasks. The objectives of the course is to introduce the basic probabilistic models and methods for making decision under risk and uncertainty; to develop initial abilities and skills to model and analyze decision problems and to use software tools in solving decision problems.

DESCRIPTION OF THE COURSE: The main topics of the course are concerned with the following optimization tasks: Linear Programming, Quadratic Programming, Genetic Algorithms, Linear Matrix Inequalities in Control Theory, basics of the Finite Elements Method, Methods for global search. The stochastic models for Decision Making under risk and uncertainty are related to the Markov Chains, Markovian Decision Process, Dynamic Programming and Linear Programming solutions of the Markovian Decision Problem; Queuing Models – Specialized Poisson Queues, Non-Poisson Queues, Queues with Priorities, Series Queues. Applications of probabilistic models for solving decision problems, related to Production and Operations Management.

PREREQUISITES: Linear algebra, Numerical methods in the linear algebra, Mathematical analysis, Identification, Control Theory, Operations Research, Industrial Management. TEACHING METHODS: Lectures, laboratory and course work using software Matlab and Arena, assignment of individualized problems and course work description preparation and defence

METHOD OF ASSESSMENT: Two written tests, that consist in solving problems at mid and end of semester (60%), laboratories (20%), course work (20%).


BIBLIOGRAPHY: 1. Gill, P.E., W. Murray, M.H. Wright (1981), Practical Optimization. Academic Press, 1981. 2. Forsythe, G., M. Malcolm, C. Moler (1986), Computer methods for Mathematical Computations, Sofia, Nauka, 1986 (in Bulgarian). 3. Hillier, F. S., G. Liebermann, (2005), Operations Research, 8th edition, 2005. 4. Taha, H., (2008), Operations Research, Prentice Hall, ISBN 978-81-317-1104-0, 2008. 5. Gatev, G., (2003), Operations Reseаrch, TU-Sofia, ISBN 954-438-089-2, 2008 (in Bulgarian) 6. Ton van den Boom. (1999), Optimisation in Systems and Control. TU – Delft, 1999.



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DESCRIPTION OF THE COURSE - oldweb.tu-sofia.bgoldweb.tu-sofia.bg/eng_new/ECTS/ectas/ectas12-13/fa/MAICE_EN/char... · To develop skils for description of uncertain systems, ... mode