English

Every autumn semester

The aim of the course is to provide advanced knowledge and skills in mathematical modeling based on measurement data, including model structure selection, parameter estimation, model validation, prediction, simulation, and control.

Lectures: Transient analysis; Spectral methods; Frequency analysis; Linear regression; Interactive programs; Model parameterizations; Prediction error methods; Instrument variable methods: Real-time identification; Recursive methods; Continuous-time models, Identification in closed loop; Structure selection; Model validation; Experiment design; Model reduction; Partitioned models; 2D-methods; Nonlinear systems; Subspace methods;
Laboratories: Frequency analysis, Interactive identification, Identification for control

be able to define basic concepts for systems with multiple inputs and outputs
be able to translate between different multivariable system descriptions, in particular time series models, transient responses, transfer function matrices, and state-space descriptions
be able to derive dynamical mathematical models describing relations between inputs and outputs, including disturbance models
understand the role of the experimental conditions for the accuracy and quality of the resulting mathematical model
be able to approximate (reduce) multivariable mathemical models according to a given approximation accuracy

be able to formulate control-oriented models of multivariable systems in the form of state-space models, time series models, transient responses, and transfer function
be able to calculate dynamic mathematical models from experimental input and output signal measurements
be able to validate a mathematical model in relation to experimental data using statistical analysis, model approximation, and simulation
be able to translate control specifications to requirements on the mathematical model

be able to understand relations and limitations when simplified models are used to describe a complex multivariable real system
show ability for teamwork and group collaboration during projects

Lectures

Laboratory exercises

Exercises

Written exam

Written assignments

Failed, pass

FRT010 Automatic Control, Basic Course, FMSF10 Stationary Stochastic Processes

Johansson, R.: System Modeling and Identification. Prentice Hall, 1993. ISBN 0134823087.

FRT145F

 -10-27

Professor Thomas Johansson