ELE653 - ADAPTIVE CONTROL
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| ADAPTIVE CONTROL | ELE653 | Any Semester/Year | 3 | 0 | 3 | 8 |
| Prequisites | None | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Question and Answer Problem Solving | |||||
| Instructor (s) | Department Faculty | |||||
| Course objective | Control 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 |
| |||||
| Course Content | System 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. | |||||
| References | 1. 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
| Weeks | Topics |
|---|---|
| Week 1 | An overview of adaptive systems, Model Reference Control and solution of Diophantine equation. |
| Week 2 | Model Reference Adaptive Control: Gradient approach and MIT rule. |
| Week 3 | Model Reference Adaptive Control: Stability, error and parameter convergence and modified adjustment rules. |
| Week 4 | Model Reference Adaptive Control based on stability theories and SPR rule. |
| Week 5 | Least Squares parameter estimation, Recursive Least Squares (RLS) and Extended Least Squares. |
| Week 6 | Tracking parameter changes, covariance resetting, random walk, forgetting factor approach, covariance blow-up, directional and variable forgetting factors. Gradient methods for parameter estimation. |
| Week 7 | Parameter estimation for continuous-time models and continuous-time least squares. |
| Week 8 | Self-tuning control: model reference method. |
| Week 9 | Self-tuning control: Minimum Variance (MV) and Generalized minimum Variance (GMV) method. |
| Week 10 | Midterm Exam |
| Week 11 | Self-tuning control: Generalized Predictive Control (GPC) method. |
| Week 12 | Continuous-time Self-tuning control. |
| Week 13 | Auto-tuning. |
| Week 14 | Gain scheduling. |
| Week 15 | Final exam. |
| Week 16 | Final exam. |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 6 | 30 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 1 | 30 |
| Final exam | 1 | 40 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 0 | 60 |
| Percentage of final exam contributing grade succes | 0 | 40 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 13 | 3 | 39 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 5 | 70 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 6 | 8 | 48 |
| Midterms (Study duration) | 1 | 25 | 25 |
| Final Exam (Study duration) | 1 | 25 | 25 |
| Total Workload | 35 | 66 | 207 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 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 effectively | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest