1. Di Ruscio, D. (2002).Subspace system identification.Theory and applications.  Lecture notes (pdf-file). 
  2. State estimation and the Kalman filter. Lecture notes (pdf-file). 21 pages. We skip sections 2.1, 2.3, 2.4 and 2.5. 
  3. Further notes on the state observer. MATLAB files: Function to find the gain, K: lpe2.m  lpe.m, ss2ocf.m. Solution ex. 5.1: ex_id_simp2.m File to generate input experiment: prbs1.m
  4. Prediction error methods. Chapter 14  lecture notes. (pdf-file). Directly from this file: Ch. 14
  5. Di Ruscio, D. (2005). Subspace system identification of the Kalman filter: Open and closed loop systems. Paper
  6. Di Ruscio, D- (2001). An introduction to MATrix LABoratory (MATLAB). Lecture notes
  7. Additional support lecture notes on System Identification, Ljung (1995)
  8. Development of System Identification. Ljung (1996).
  9. A view on the Unscented Kalman Filter (UKF) algorithm and comparison with the Extended Kalman Filter (EKF). Paper
  10. Closed and Open Loop Subspace System Identication of the Kalman Filter

The course syllabus is mainly taken from enumerate:  1 and 5 (subspace system identification), 2 and 3 (state estimation and Kalman filter) and 4 (prediction error methods for system identification). See also the Lecture Plan for details. Item 7 and 8 gives an additional overview of the field of System identification.

For possible post-script files: Notes about how to read post script (*.ps) files and to covert from ps-files to pdf-files (use Gsview):

  • Post-script (*.ps) files can be read and printed to our post script printers by the gsview software. Note. A new version of gsview is available and may be downloaded from the internet.





Faglærer:  Dr. ing., 1. amanuensis David Di Ruscio                          

Oppdatert 5.01.2018 av david.di.ruscio@usn.no