Course Information

Course Name	Turkish	Öğrenme Tabanlı Kontrol Sistemlerine Giriş
	English	Introduction to Learning based Control Systems
Course Code	KON 442E	Credit	Lecture (hour/week)	Recitation (hour/week)	Laboratory (hour/week)
Semester	8	3	3	-	-
Course Language		English
Course Coordinator		Kemal Uçak
Course Objectives		Understand the fundamental differences between classical and learning-based adaptive control methods. Learn stability concepts such as Lyapunov stability and apply them in adaptive control design. Develop skills to design model reference adaptive controllers for different system orders. Gain knowledge about machine learning-based control methods, including neural networks. Understand robust and advanced adaptive control methods and explore their real-world applications.
Course Description		Introduction, classical control and learning-based control, basic mathematical concepts, Lyapunov stability, MRAC design, adaptive dynamic inversion(ADI) control, Parameter convergence, MRAC design modifications for robustness, Parameter drift, Projection-based MRAC, machine learning-based approaches for MRAC, approximation-based adaptive control, MRAC for dynamic systems with unstructured and unmodeled dynamics, adaptive backstepping controller, concurrent and composite learning-based adaptive control, advanced control techniques using machine learning
Course Outcomes		Explain the basic concepts of adaptive and learning-based control systems. Apply Lyapunov stability theory to analyze the stability of adaptive systems. Design MRAC controllers and tune adaptive parameters for dynamic systems. Implement machine learning-based control approaches using neural networks. Evaluate the performance of adaptive controllers in practical scenarios.
Pre-requisite(s)		EEF 281 MIN. DD or EEF 281E MIN. DD )
Required Facilities
Other
Textbook		Eugene Lavretsky, Kevin A. Wise (2024) Robust and Adaptive Control With Aerospace Applications. Second Edition- Springer- Advanced Textbooks in Control and Signal Processing
Other References		Karl Johan Åström, Björn Wittenmark (2008) Adaptive Control. Dover Publication Ryan G. McClarren (2021) Machine Learning for Engineers: Using data to solve problems for physical systems. Springer Simon Haykin (1999) Neural Networks: A Comprehensive Foundations . Pearson Education

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