Systems and Control Theory

Description

The student is able to apply the basic time-domain system identification procedure for linear time-invariant systems with one input and one output. This includes: selection of a system model structure, selection of a system excitation, linear-least squares model parameter estimation and validation of the model accuracy. The student is able to linearize a nonlinear system, that is, to derive an approximate linear model for a given or to be determined equilibrium state. The student is able to analyze continuous and discrete time system in time domain and in frequency domain, based on different system representations, e.g. a transfer function or a state space model description. The student is able to analyze (periodic, non-periodic continuous and discrete time) signals in the frequency domain, knows how to sample signals and to select an appropriate sampling rate. The student is able to transform a continuous time model to discrete time and is familiar with aliasing. The student is able to analyze the stability of systems, controllability and observability of state space models. The student knows the difference between the different types of stability. The student can calculate the energy dissipation in a linear system. For a given linear time-invariant single-input single-output system and given design specification in time domain or frequency domain, the student is able to design a classical compensator using frequency-domain methods (choosing a type of compensator and determining its parameters); design a compensator based on state-feedback, including a closed-loop state estimator; add feed forward to a classical compensator or a state-feedback compensator to eliminate steady-state errors on various input signals; evaluate the designs above critically. The student is able to design a Kalman filter to estimate the states of a linear or nonlinear system optimally, and to evaluate the design critically.