ECTSEstimation and Identification for Engineering Systems

Estimation and Identification for Engineering Systems (MDES630) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Estimation and Identification for Engineering Systems MDES630 Elective Courses 3 0 0 3 5
Pre-requisite Course(s)
Consent of the instructor
Course Language English
Course Type Elective Courses Taken From Other Departments
Course Level Ph.D.
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives At the end of the course students will gain the ability to design filters for state estimation and will experience the implementation of the designed filters on physical systems. Also, some techniques in system identification will be discussed and students will practice experiments for system identification.
Course Learning Outcomes The students who succeeded in this course;
  • To develop concepts of estimation and identification for engineering systems. To give the experience of implementing estimation and identification algorithms on real experimental data.
Course Content Kalman filtering, nonparametric identification techniques and parameter estimation methods; implementation of filtering algorithms on physical systems and collecting data from real systems.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 State estimation, review of observers -
2 Kalman filter -
3 Extended Kalman filter -
4 Unscented Kalman filter -
5 Unscented Kalman filter -
6 Case studies -
7 Case studies -
8 Concepts in system identification -
9 Nonparametic methods, parameter estimation methods -
10 Least squares estimation -
11 Maximum likelihood estimation -
12 Prediction error method -
13 Neural networks for identification -
14 Case studies -
15 Overall review -
16 Final exam -

Sources

Course Book 1. Kumar, P. R., Varaiya, P., Stochastic Systems: Estimation, Identification, and Adaptive Control, Prentice Hall, 1986.
Other Sources 2. Ljung, L., System Identification, Theory for the User, PTR Prentice Hall, New Jersey, 1987.
3. Maybeck, P. S., Stochastic Models, Estimation, and Control, Academic Press, 1979.
4. Minkler G., Minkler J. Theory and Application of Kalman Filtering, Magellan Book Company, USA, 1993.
5. Nelles O., Nonlinear System Identification from Classical Approaches to Neural Networks and Fuzzy Models, Springer, 2001

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments - -
Presentation - -
Project 2 40
Report - -
Seminar - -
Midterms Exams/Midterms Jury 2 40
Final Exam/Final Jury 1 20
Toplam 5 100
Percentage of Semester Work 80
Percentage of Final Work 20
Total 100

Course Category

Core Courses
Major Area Courses X
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Gains the ability to understand and apply knowledge in the fields of mathematics, science and basic sciences at the level of expertise.
2 Gains the ability to access wide and deep knowledge in the field of Engineering by doing scientific research with current techniques and methods, evaluate, interpret and implement the gained knowledge.
3 Being aware of the latest developments his/her field of study, defines problems, formulates and develops new and/or original ideas and methods in solutions.
4 Designs and applies theoretical, experimental, and model-based research, analyzes and interprets the results obtained at the level of expertise.
5 Gains the ability to use the applications, techniques, modern tools and equipment in his/her field of study at the level of expertise.
6 Designs, executes and finalizes an original work process independently.
7 Can work in interdisciplinary and interdisciplinary teams, lead teams, use the information of different disciplines together and develop solution approaches.
8 Pays regard to scientific, social and ethical values in all professional activities and acquires responsibility consciousness at the level of expertise.
9 Contributes to the literature by communicating the processes and results of his/her academic studies in written form or orally in national and international academic environments, communicates effectively with communities and scientific staff working in the field of specialization.
10 Gains the skill of lifelong learning at the level of expertise.
11 Communicates verbally and in written form using a foreign language at least at the European Language Portfolio B2 General Level.
12 Recognizes the social, environmental, health, safety, legal aspects of engineering applications, as well as project management and business life practices, being aware of the limitations they place on engineering applications.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 2 32
Presentation/Seminar Prepration
Project 3 10 30
Report
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 2 8 16
Prepration of Final Exams/Final Jury 1 10 10
Total Workload 136