After successful completion of the course, students are able to:

• identify dynamic systems by means of non-parametric and parametric identification methods.
• design optimal state observers (Kalman filter, extended Kalman filter, unscented Kalman filter).
• design optimal linear state controllers (LQR).
• understand and reproduce the mathematical methods required for the design of optimal state observers and controllers.

• non-parametric identification methods (transient response analysis, fourier analysis ETFE)
• basics of stochastics
• parametric identification methods (least squares with and without stochastic perturbation)
• model structures for identification (ARMA, ARX, ARMAX)
• Recursive Least Squares (RLS) method, Least Mean Squares (LMS) concept
• weighted least squares method
• optimal estimation (Gauß-Markov estimation, Minimum-Variance estimation)
• optimal observer (Kalman-filter)
• optimal nonlinear observer (extended Kalman-filter, unscented Kalman-filter)
• dynamic programming due to Bellman
• Linear Quadratic Regulator (LQR) problem with finite and infinite time horizon
• optimal output regulator (Linear Quadratic Gaussian (LQG) problem), separation principle for the optimal controller and observer design
• advanced concepts of state-space control (feedforward of the estimated perturbation, state-space controller with integral part, servoproblem)

lecture, solution of examples during the lecture, independent solution of examples of the lecture notes by the students

The lecture is planned as a lecture at TU Wien. If, due to the COVID situation a lecture at TU Wien is not possible, we will switch to an online lecture in TUWEL (ZOOM). The first lecture will take place at 06.10.2021, 9:00 in the room EI4. Please note that a registration in the course is necessary in order to access the TUWEL (ZOOM) course.

Lecture Notes (in German) are available at https://www.acin.tuwien.ac.at/master/regelungssysteme/ .

oral exam

Please register for the course due to administrative reasons.

This course is based on the concepts taught in VU Automatisierung or a similar course on basics of control engineering. Furthermore, basic knowledge in linear algebra and stochastics is strongly recommended.