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

• identify dynamic systems by means of no-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), 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

oral exam (online)

The registration via TISS is used for administrative purposes only.

Lecture Notes (in German) are available on the homepage of the institute. The link will be sent to the registered participants via News information.

This course is based on the concepts taught in VU Automatisierung. Furthermore, basic knowledge in linear algebra and stochastics is strongly recommended.