Modeling and System Identification

Prof. Moritz Diehl, Katrin Baumgärtner

 

Modeling and System Identification (MSI) is concerned with the search for mathematical models for real-life systems. The course is based on statistics, optimization and simulation methods for differential equations. The exercises will be based on pen-and-paper exercises and computer exercises with MATLAB. You can get a Matlab Student license via the University, please check here

Course language is English and all course communication is via this course homepage.

If you have any questions regarding the exercises/lectures, please send an email to the tutors, syscop.msi@gmail.com

If you have any feedback or questions about course organization write to katrin.baumgaertner@imtek.uni-freiburg.de


Lectures. The lectures will take place on Mondays, 8:00 - 10:00 a.m and Wednesdays, 9:00-10:00. It is possible to attend both in person, Building 101, HS 036, and remotely via Zoom

  • https://uni-freiburg.zoom.us/j/62699713471?pwd=Y1RQajRrT21DNFVJelRuQjVvK1AwUT09
  • Passcode: MSIWS2021

If you cannot attend, you may watch the lecture recordings, see below.


Exercises. The exercise sheets include both pen-and-paper exercises as well as programming exercises using Matlab. Exercise sheets can be handed in during the Q&A session on Mondays or might be uploaded to the course ILIAS page. You have one week to work on the sheet and you might work in groups of at most three students. Programming exercises should be handed in via Matlab Grader.

There are three exercise sessions.

During the exercise session, the exercise solutions are discussed. Afterwards there is room for questions on the current exercise sheet.

Please join the ILIAS course: https://ilias.uni-freiburg.de/goto.php?target=crs_2368593_rcodeaeYdrEHFgD&client_id=unifreiburg

Written material. The lecture closely follows the script, which can be found below:

Please note that we do not cover Chapter 8 and Chapter 9.4. Additional material that covers some of the lecture contents:

  • A script by Johan Schoukens (Vrije Universiteit Brussel, Belgium), which can be found here.
  • The textbook Ljung, L. (1999). System Identification: Theory for the User. Prentice Hall. This book is available in the faculty library.

Final Evaluation and Microexams

Please make sure you register for both the MSI Exam and the MSI Studienleistung!

The final grade of the course is based solely on a final written exam at the end of the semester. The final exam is a closed book exam, only pencil, paper, and a calculator, and two handwritten double-sided A4 sheets of self-chosen formulae are allowed.

Each exercise sheet gives a maximum of 10 points. Three online microexams written during some of the lecture slots give a maximum of 10 exercise points each. In order to pass the exercises accompanying the course (Studienleistung), one has to obtain at least 50% of the maximum exercise points in each of the three blocks:

  • Block 1: Exercises 1 - 3 + Microexam 1 (total 40 points)
  • Block 2: Exercises 4 - 6 + Microexam 2 (total 40 points)
  • Block 3: Exercises 7 - 10 + Microexam 3 (total 50 points)

To prepare for the written exam, check out the exams from previous semesters: 2018, 2015, 2014. (Please note that these exams contain questions on Chapter 8 of the MSI script, which is not covered in this year's lecture)


Lectures and Microexams

Monday, October 18, 8:00-10:00 Intro session QR
Wednesday, October 20, 9:00-10:00 Lecture QR
Monday, October 25, 8:00-10:00 Lecture QR
Wednesday, October 27, 9:00-10:00 Lecture QR
Wednesday, November 10, 9:00-10:00 Guest Lecture: Andrea Gezzi QR
Monday, November 8, 8:00-10:00 Lecture QR
Wednesday, November 10, 9:00-10:00 Lecture QR
Monday, November 15, 8:00-10:00 Lecture QR
Wednesday, November 17, 9:00-10:00 Microexam 1 on Chapter 1-4.2 (online)  
Monday, November 22, 8:00-10:00 Lecture QR
Wednesday,  November 24, 9:00-10:00 Lecture QR
Monday, November 29, 8:00-10:00 Lecture QR
Wednesday, December 01, 9:00-10:00 Lecture QR
Monday, December 6, 8:00-10:00 Lecture QR
Wednesday, December 8, 9:00-10:00 Lecture QR
Monday, December 13, 8:00-10:00 Lecture QR
Wednesday, December 15, 9:00-10:00 Lecture QR
Monday, December 20, 8:00-10:00 Lecture QR
Wednesday, December 22, 9:00-10:00 Microexam 2 on Chapter 4.2-5.4 (online)  
Monday, January 10, 8:00-10:00 Lecture QR
Wednesday, January 12, 9:00-10:00 Lecture QR
Monday, January 17, 8:00-10:00 Lecture QR
Wednesday, January 19, 9:00-10:00 Lecture QR
Monday, January 24, 8:00-10:00 Lecture QR
Wednesday, January 26, 9:00-10:00 Microexam 3 on Chapter 6.3-7, 9-9.3 (online)  
Monday, January 31, 8:00-10:00 Lecture QR
Wednesday, February 2, 9:00-10:00 Lecture QR
Monday, February 7, 8:00-10:00 Summary Session QR

Lecture Recordings

date topic chapters
October 18 - October 22 Lecture 1: Introduction + Resistance Estimation 1-1.2
October 25 - October 29 Lecture 2: Resistance Estimation + Statistic Basics 1.2.2-2.3
October 25 - October 29 Lecture 3: Random Variables + Statisitical Estimators 2.3-2.4
November 8 - November 12 Lecture 4: Resistance Estimation Revisited 2.5-3.1
November 8 - November 12 Lecture 5: Optimization Basics + Linear Least Squares 3.1-4.2
November 15 - November 19 Lecture 6: WLS + Ill-posed Problems 4.3-4.4.1
November 29 - December 3 Lecture 7: Statistical Analysis of WLS 4.5-4.7
November 29 - December 3 Lecture 8: Maximum Likelihood Estimation 5-5.1.1
December 6 - December 10 Lecture 9: MAP Estimation + Recursive LLS 5.2-5.3.2
December 6 - December 10 Lecture 10: Cramer Rao Bound 5.3-5.4
December 13 - December 17 Lecture 11: Practical Solution of NLS (Part 1, Part 2) 5.5.-6.2.2
January 10 - January 14 Lecture 12: Dynamic System Classes 6.2.3-6.5.2
January 10 - January 14 Lecture 13: Output and Equation Errors 7.1-7.3
January 17 - January 21 Lecture 14: State Space Models 7.4
January 17 - January 21 Lecture 15: RLS + Kalman Filter 9.1-9.3
January 24 - January 28 Lecture 16: Extended Kalman Filter 9.5
January 31 - February 4 Lecture 17: Moving Horizon Estimation 9.6

Exercises and Tutorials

In the first week, there is no exercise sheet, but if you don't feel too confident about your linear algebra and statisitics skills, you might want to check out these tutorials that cover the basics needed for the MSI course.

You can get a Matlab Student license via the University, please check here

Sheet Material Deadline
Sheet 1: Linear Algebra Basics + Estimator Example data November 1
Sheet 2   November 8
Sheet 3   November 15
Sheet 4   November 22
Sheet 5   December 6
Sheet 6   December 13
Sheet 7   January 10
Sheet 8   January 17
Sheet 9   January 31
Sheet 10   February 7

Check In for exercise sessions: