The course offers students to learn basic and advanced approaches of robust control of continuous-time linear time-invariant systems. Besides of the theory, a significant emphasis is given on the practical application of the available robust control tools in Matlab.
2018 with G. Mazzoccante and P.J.W. Koelewijn
2017 with dr. M. Schoukens and G. Mazzoccante
2014-16 with J. Hanema and I. Promiadis
2013-14 with dr. Dario Piga
2012-13 with dr. Mircea Lazar and dr. N. Athanasopoulos
The goal of this course is to integrate and apply the theoretical knowledge gained in past courses on control design of a laboratory setup, giving hands on experience to students on practical identification and advanced control design.
2013-18 with prof. dr. S. Weiland & dr. L. Ozkan
The goal of this course is to provide the student with a comprehensive overview of the main off-the-shelf machine learning techniques for black-box nonlinear model identification and control, and to give the fundamental tools for practical implementation of these techniques. By taking this course, the student masters the main machine learning based modelling and control techniques for nonlinear systems, namely kernel methods, Gaussian process regression, neural networks and control policy learning methods and develops the required skills to implement them for online learning purposes.
2018 with dr. M. Schoukens
Linear matrix inequalities for control
The course offers an advanced overview of the use of Linear Matrix Inequalities in systems and control for postgraduate and Phd students. The power of the LMI approach is illustrated by several fundamental robustness and performance problems in analysis and design of linear control systems.
4AT900 Stochastic Signal Analysis and Estimation
The goal of this course is to overview the basic principles of describing and modeling of probabilistic (random) signals together with the notion of correlation functions and spectral densities and discussing filtering and estimation of stochastic signals/parameters in terms of the Maximum Likelihood principle, least-squares estimators and Wiener filters.
5CC70 Adaptive Systems
The course offers students to learn basic approaches of adaptive signal processing and control together with basic data-driven modeling tools, i.e., system identification approaches.
WB4432-05 Process Dynamics and Control
The course offers students to learn basic and advanced approaches of the Systems & Control theory that are relevant for dynamic modeling, simulation and control of chemical and energy conversion processes.
Sc4050 Integration Project
The goal of this course is to integrate and apply the theoretical knowledge gained in past courses giving hands on experience to students about practical identification and advanced control design.
Wb3250 Signal Analysis
General theory of signal analysis: Fourier series expansion, Fourier transform (DT/CT/DFT), Laplace transform, Sampling, Filtering etc. (Teaching practical sessions)
Sc4070 Control Systems Lab
Lab works oriented to give hands on experience to students about practical identification, PID and advanced MIMO control design.