ACADEMICS
Course Details
ELE 601 Linear System Theory
2018-2019 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.
ELE601 - LINEAR SYSTEM THEORY
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| LINEAR SYSTEM THEORY | ELE601 | Any Semester/Year | 3 | 0 | 3 | 8 |
| Prerequisite(s) | 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 | Many engineering problems can be analyzed and solved within the framework of system concept, which is a very fundamental notion in engineering. It is possible to classify systems into two main groups as liner and nonlinear although they may have many different properties and characteristics. Systems can be assumed as linear under certain conditions despite the fact that most of the systems are nonlinear. In this way, linear systems point of view can also be used in the analysis of nonlinear systems. In this course, the aim is to provide the necessary background for the students to be able to understand and solve the engineering problems by using the theory and methods developed for linear systems. | |||||
| Learning outcomes |
| |||||
| Course Content | Linear spaces. Change of basis. Linear operators. Range space and null space. Eigenvalues and eigenvectors. Jordan form representation. Function of a square matrix. Norms. Linear system description: input-output and state variable descriptions, time invariant and time varying systems. Modal decomposition. Equivalent (or similar) systems and equivalence (or similarity) transformation. Linear system analysis: controllability, observability and stability. | |||||
| References | 1. Chen C.T., Linear System Theory and Design, HRW, 1984. 2. Kailath T., Linear Systems, Prentice Hall, 1980. 3. Decarlo R.A., Linear Systems: A state variable approach with numerical implementation, Prentice Hall, 1989. 4. Rugh W.J., Linear System Theory, 2nd Ed., Prentice Hall, 1996. 5. Brogan W.L., Modern Control Theory, 3rd Ed., Prentice Hall, 1991. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Linear spaces : field, linear space, subspace, span, linear independence, dimension, basis, change of basis. |
| Week 2 | Linear oprerators and representations of a linear operator. |
| Week 3 | Linear operators: range and null spaces, eigenvalues and eigenvectors, Jordan form representation. |
| Week 4 | Polynomial of a square matrix, minimal polynomial, function of a square matrix, norms and inner product. |
| Week 5 | Linear system description: input-output approach (for both time-invariant and time varying). |
| Week 6 | Linear system description: state variable approach (for both time-invariant and time varying). |
| Week 7 | Solution of dynamical equations, fundamental martix and state transition matrix. |
| Week 8 | Solution of dynamical equation, computation of eAt and (SI-A)-1, Faddeev algorithm, modal decomposition. |
| Week 9 | Equivalent (or similar) systems and equivalence (or similarity) transformation. |
| Week 10 | Midterm Exam |
| Week 11 | Linear system analysis: Controllability and observability. |
| Week 12 | Linear system analysis: Controllability and observability. |
| Week 13 | Linear system analysis: Stability. |
| Week 14 | Linear system analysis: Stability. |
| 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 | 10 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 1 | 40 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 0 | 50 |
| Percentage of final exam contributing grade succes | 0 | 50 |
| 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 | 6 | 36 |
| Midterms (Study duration) | 1 | 25 | 25 |
| Final Exam (Study duration) | 1 | 30 | 30 |
| Total Workload | 35 | 69 | 200 |
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