Department of Electrical and Electronics Engineering

Course Details

ELE 653 Adaptive Control2019-2020 Summer term information

The course is not open this term

Timing data are obtained using weekly schedule program tables. To make sure whether the course is cancelled or time-shifted for a specific week one should consult the supervisor and/or follow the announcements.

Course definition tables are extracted from the ECTS Course Catalog web site of Hacettepe University (http://akts.hacettepe.edu.tr) in real-time and displayed here. Please check the appropriate page on the original site against any technical problems. Course data last updated on 07/08/2020.

Course Name Code Semester Theory
(hours/week)
Application
(hours/week)
Credit ECTS
ADAPTIVE CONTROL ELE653 Any Semester/Year 3 0 3 8
Prerequisite(s)None
Course languageTurkish
Course typeElective
Mode of DeliveryFace-to-Face
Learning and teaching strategiesLecture
Problem Solving

Instructor (s)Department Faculty
Course objectiveControl systems are usually designed by assuming that the system parameters are not changing. However, in many practical applications system parameters are not constant but changes with time and this affects the control performance adversely. Control systems that have the ability to sense the changes in the system parameters and to change itself accordingly in order to maintain a certain desired performance are called adaptive. In this course, the aim is to equip students with the necessary knowledge and skills in order to be able to understand, analyze and design such systems.
Learning outcomes
1. A student completing the course successfully is expected to
2. L.O.1. understand the nature of uncertainties affecting a system.
3. L.O.2. be able to identify (to model) systems using experimental data.
4. L.O.3. be able to decide whether an adaptive control is a good option for a given problem.
5. L.O.4. be able to analyse and design adaptive control systems.
6. L.O.5. have a suitable background to follow further studies in adaptive systems.
Course ContentSystem models. Parameter estimation: Least Squares method, Recursive Least Squares (RLS), Extended Recursive Least Squares (ERLS), parameter tracking, covariance blow-up, gradient methods. Model reference adaptive control: MIT and SPR rules. Self-tuning control: Model reference control, Minimum Variance (MV) method, Generalized Minimum Variance (GMV), Generalized Predictive Control (GPC).
Continuous-time Self-tuning control. Auto-tuning and gain scheduling. Stability, convergence and robustness.

References1. Astrom K.J. and Wittenmark B., Adaptive Control, 2nd Ed., Addison Wesley, 1995.
2. Wellstead P.E. and Zarrop M.B., Self-Tuning Systems, Wiley, 1991.
3. Narendra K.S. and Annaswamy A.M., Stable Adaptive Systems, Prentice Hall, 1989.
4. Sastry S. And Bodson M., Adaptive Control: Stability, Convergence, and Robustness, Prentice Hall, 1989.
5. Gawthrop P.J., Continuous-Time Self-Tuning Control, Research Studies Press, 1987.
6. Ljung L. And Söderström T., Theory and Practice of Recursive Identification, MIT Press, 1983.

Course outline weekly

WeeksTopics
Week 1An overview of adaptive systems, Model Reference Control and solution of Diophantine equation.
Week 3Model Reference Adaptive Control: Stability, error and parameter convergence and modified adjustment rules.
Week 4Model Reference Adaptive Control based on stability theories and SPR rule.
Week 5Least Squares parameter estimation, Recursive Least Squares (RLS) and Extended Least Squares.
Week 6Tracking parameter changes, covariance resetting, random walk, forgetting factor approach, covariance blow-up, directional and variable forgetting factors. Gradient methods for parameter estimation.
Week 7Parameter estimation for continuous-time models and continuous-time least squares.
Week 8Self-tuning control: model reference method.
Week 9Self-tuning control: Minimum Variance (MV) and Generalized minimum Variance (GMV) method.
Week 10Midterm Exam
Week 11Self-tuning control: Generalized Predictive Control (GPC) method.
Week 12Continuous-time Self-tuning control.
Week 13Auto-tuning.
Week 14Gain scheduling.
Week 15Final exam.
Week 16Final exam.

Assesment methods

Course activitiesNumberPercentage
Attendance00
Laboratory00
Application00
Field activities00
Specific practical training00
Assignments630
Presentation00
Project00
Seminar00
Midterms130
Final exam140
Total100
Percentage of semester activities contributing grade succes060
Percentage of final exam contributing grade succes040
Total100

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 13 3 39
Laboratory 0 0 0
Application000
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)14570
Presentation / Seminar Preparation000
Project000
Homework assignment6848
Midterms (Study duration)12525
Final Exam (Study duration) 12525

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
12345
1. Has general and detailed knowledge in certain areas of Electrical and Electronics Engineering in addition to the required fundamental knowledge.    X
2. Solves complex engineering problems which require high level of analysis and synthesis skills using theoretical and experimental knowledge in mathematics, sciences and Electrical and Electronics Engineering.    X
3. Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems.   X
4. Designs and runs research projects, analyzes and interprets the results.   X
5. Designs, plans, and manages high level research projects; leads multidiciplinary projects.   X
6. Produces novel solutions for problems.   X
7. Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects.   X
8. Follows technological developments, improves him/herself , easily adapts to new conditions.   X
9. Is aware of ethical, social and environmental impacts of his/her work.X
10. Can present his/her ideas and works in written and oral form effectively; uses English effectivelyX

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest