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| EEE4022 | @EEE4022 Intelligent Control System | 2+0+0 | ECTS:5 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Elective | | Department | DEPARTMENT of ELECTRICAL and ELECTRONICS ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 2 hours of lectures per week | | Lecturer | Doç. Dr. Mustafa Şinasi AYAS | | Co-Lecturer | Asst. Prof. Yeşim Aysel BAYSAL ASLANHAN | | Language of instruction | | | Professional practise ( internship ) | None | | | | The aim of the course: | | The goal is to teach students modern and advanced intelligent control techniques beyond traditional control methods. This course aims to equip students with the skills to design, analyze, and implement control systems using methods such as fuzzy logic and reinforcement learning. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Understanding the Fundamentals of Intelligent Control Systems: Students will learn the basic principles and concepts of intelligent control systems. | 1,2 | 1,3, | | LO - 2 : | Designing Control Systems Using Fuzzy Logic: Students will be able to design various control systems using the principles of fuzzy logic. | 2,3,7 | 1,3, | | LO - 3 : | Applying Reinforcement Learning Methods: Students will be able to apply reinforcement learning techniques to control systems and evaluate the performance of these techniques. | 2,3,7 | 1,3, | | LO - 4 : | Gaining Simulation and Analysis Skills: Students will be able to simulate and analyze control systems to assess the dynamic behavior of these systems. | 4,5 | 1,3, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | | |
| Overview of Intelligent Control Systems, Comparison of traditional control systems and intelligent control systems and their applications, Definition and history of fuzzy logic, fuzzy sets and membership functions, Fuzzification and fuzzy inference, Structure, design, and applications of fuzzy logic controllers, Definition and history of reinforcement learning, Fundamental concepts in reinforcement learning: Agent, Environment, Reward, State, and Action, Markov Decision Processes (MDP), Q-Learning Algorithm, Application of reinforcement learning in control systems |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction to intelligent control Systems, Fundamental concepts and history, comparison of traditional and intelligent control systems | | | Week 2 | A brief history of fuzzy logic and the concept of fuzziness | | | Week 3 | The definition of fuzzy sets, membership functions, and application examples | | | Week 4 | Basic fuzzy operations and fuzzy relations | | | Week 5 | Fuzzy clustering and partitioning | | | Week 6 | Fuzzy rule-based systems and fuzzy decision making, Mamdani fuzzy modeling | | | Week 7 | Sugeno and Tsukamoto fuzzy modeling, defuzzification methods | | | Week 8 | Fuzzy logic controller structure and design | | | Week 9 | Midterm exam | | | Week 10 | An application example of fuzzy logic controller | | | Week 11 | Reinforcement Learning: Definition, historical background, distinctions from other machine learning techniques, applications, and examples | | | Week 12 | Key Concepts in Reinforcement Learning: Agent, environment, reward, state, and action | | | Week 13 | Markov Decision Processes: Definition, properties, and areas of use | | | Week 14 | Q-Learning Algorithm: Basic principles, and application examples | | | Week 15 | Applications of reinforcement learning in control systems and examples | | | Week 16 | Final Exam | | | |
| 1 | Altaş, İ. H. 2017; Fuzzy Logic Control in Energy Systems: with Design Applications in Matlab/Simulink®, IET Books, London | | | |
| 1 | Liu, J. 2018; Intelligent control design and Matlab simulation, Springer, Singapore | | | 2 | Hangos, K.M., Lakner, R., and Gerzson M. 2001; Intelligent control systems: an introduction with examples, Kluwer Academic Publishers | | | 3 | Passino, K. M. and Yurkovich, S. 1998; Fuzzy Control, MA: Addison-Wesley | | | 4 | Sutton, R. S. and Barto, A. G. 2018; Reinforcement learning: An introduction, MIT press | | | 5 | Meyn, S. 2022; Control systems and reinforcement learning, Cambridge University Press | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 2 | 30 | | Homework/Assignment/Term-paper | 3
5
7
11
13 | | 5 | 20 | | End-of-term exam | 16 | | 2 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 2 | 14 | 28 | | Sınıf dışı çalışma | 3 | 14 | 42 | | Arasınav için hazırlık | 4 | 3 | 12 | | Arasınav | 2 | 1 | 2 | | Ödev | 5 | 5 | 25 | | Dönem sonu sınavı için hazırlık | 4 | 3 | 12 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 123 |
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