DSA Kalman Student Environment
S&C Obligatory Course Materials
Quarter 1
Control Theory (SC42015): Two textbooks, not essential to follow the course, lecture slides are enough. Having digital versions to look something up is recommended.
- B. Friedland, Control System Design: An Introduction to State-space Methods. Dover Publications, 2005.
- K.J. Astrom, R.M. Murray, Feedback Systems: An Introduction for Scientists and Engineers, Princeton University Press, Princeton and Oxford, 2009 http://www.cds.caltech.edu/~murray/amwiki/
Optimisation for Systems and Control (SC42056): The course materials are usually posted on this website rather than Brightspace. One reader, free to download from here (just search for “optimization” or the course code).
- “Lecture notes Optimization in systems and control”, by T. van den Boom and B. De Schutter, Delft, September 2025.
- List of optional additional reading can be found on the course website.
Statistical Signal Processing (SC42150): One reader downloadable from the course’s Brightspace page.
- “Lecture Notes for the Course SC42150; Statistical Signal Processing”, by Carlas S. Smith and Michel Verhaegen, Delft, September 2021
Modelling of Dynamic Systems (SC42155): One reader downloadable from the course’s Brightspace page.
- “Lecture Notes for the Course SC42155; Modelling of dynamical systems”. by Ton J.J. van den Boom, Manuel Mazo Jr. and Riccardo M.G. Ferrari, Delft, October 2024
Quarter 2
FIltering and Identification (SC42025): One essential book, extremely helpful for the open book exam, and short reader downloadable from the course’s Brightspace page.
- Filtering and System Identification: A Least Squares Approach by Michel Verhaegen and Vincent Verdult. Hardback ISBN-13: 9780521875127. Paperback ISBN-13: 978110740502
- “Lecture notes on Bayesian estimation for SC42025: Filtering & Identification”, by Manon Kok and Frida Viset, Delft, December 2023
Machine Learning for Systems and Control (SC42165): Lecture slides are considered sufficient. The following literature is listed for the course:
- Bishop, Christopher M., and Nasser M. Nasrabadi. Pattern recognition and machine learning. Vol. 4. No. 4. New York: springer, 2006.
- Sutton, R. S., Barto, A. G., Reinforcement Learning: An Introduction. The MIT Press, 2018.
- Bertsekas D., Lessons from AlphaZero for Optimal, Model Predictive, and Adaptive Control, Athena Scientific, 2022
- Hastie, T., Tibshirani, R., and Friedman, J., The Elements of Statistical Learning, 2nd Ed., Springer, New York, 2009.
Digital Control (SC42095): One textbook that can be helpful for the assignments, and additional reading/references.
- K.J. Åström, B. Wittenmark ‘Computer-controlled Systems’, Prentice Hall,1997, 3rd edition
- B.C. Kuo ‘Digital Control Systems’, Tokyo, Holt-Saunders, 1980
- G.F. Franklin, J.D. Powell ‘Digital Control of Dynamic Systems’, 1989, 2nd edition, Addison-Wesley
- L. Keviczky et. al ‘Control Engineering’, Springer, 2019
- L. Keviczky et. al ‘Control Engineering: MATLAB Exercises’, Springer, 2019
Robust Control (SC42145): One useful textbook.
- Sigurd Skogestad, and Ian Postlethwaite ‘Multivariable Feedback Control: Analysis and Design’, 2005