Model-based Identification and Estimation (etit-214)

Model-based identification and estimation
V-UE; 2+1 SWS; ECTS-Points: 4
Lecturer: Dr. Alexander Schaum


This lecture gives an introduction to identification and estimation methods by addressing the following topics:
  • System identification using first-principle  (white and grey box identification) as well as purely data-driven approaches (black box identification)
  • State and parameter estimation using observers and Kalman filters


See UnivIS (lecture ID 080084) and UnivIS (lecture ID 080086). 


In order to find appropriate dates for the oral examination please contact the lecturer under
The examination will take place in groups (<=2) and constits of the discussion of the Projects (Exercise 5, Exercise 10) as well as some general questionaire. It can be carried out within the following time intervals:
17.07 - 26.07.2019
12.08 - 30.08.2019
09.09 - 20.09.2019
For the possibility of taking the exam at the beginning/middle of october please contact the lecturer.



Lecture notes

  • Introduction
  • Part1: Linear continuous-time systems (solution and behavior)
  • Part2: Linear discrete-time systems
  • Part 3: Identification (general aspects, white-box identification)
  • Part 4: Grey-box identification (mean-squared error minimization)
  • Part 5: Black-box identification
  • Part 6: Observers for LTI systems
  • Part 7: Stochastic observer design (Kalman-Bucy-Filter)
  • Part 8: Sampled data stochastic observer design and joint state and parameter estimation of continuous-time systems





Are provided at the end of the notes to the classes (see Downloads).