determine relations between different model representations
analyse linear time-invariant systems with respect to relevant properties
design and implement simple controllers
System models: State-space forms and the solution of the state-space equation in discrete and continuous time. Sampling. Transfer functions and transfer operators. Model transformations from transfer functions to state-space models and vice versa.
System properties: Controllability and observability. Static gain. Step and impulse responses in discrete and continuous time. Frequency domain properties (connection to sampling). Stability in discrete and continuous time; asymptotic stability, bounded-input bounded-output stability, the Nyquist criterion.
Controller design: Pole placement in state-space form. State feedback with observer. PID controllers. Stability margins. Sensitivity functions. The notion of robustness. Computer implementation (sampling, aliasing).
Lectures, problem solving sessions and laboratory work.
Written examination at the end of the course. Passed laboratory course is also required.