Syllabus academic year 2009/2010
(Created 2009-08-11.)

Higher education credits: 7,5. Grading scale: TH. Level: A (Second level). Language of instruction: The course will be given in English on demand. Optional for: C4, C4sst, D4, D4sst, E4, E4ra, E4ss, F4, F4rs, I4, N4, Pi4. Course coordinator: Professor Rolf Johansson,, Inst f reglerteknik. Recommended prerequisits: FRT010 Automatic Control, Basic Course, FMS045 Stationary Stochastic Processes. The course might be cancelled if the numer of applicants is less than 10. Assessment: Written exam (5 hours), project, three hand-in problem sets, three laboratory exercises, In the case of less than 5 registered students, the second and third exam may be given in oral form. Further information: The course might be given in English. Home page:

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.

Knowledge and understanding
For a passing grade the student must

Skills and abilities
For a passing grade the student must

Judgement and approach
For a passing grade the student must

Identification is a relevant topic for everyone that is working with analysis of experimental data and mathematical modeling. The topics of identification include measurement collection, signal conditioning, model selection, parameter estimation, and mathematical modeling. The course primarily covers physical models and dynamical models represented as differential equations, transfer functions, and difference equations. Identification is important in control, where mathematical models play an important role in decision-making, prediction, control, simulation, and optimization. Many control design methods assume the existence of transfer functions that describe the controlled process. The derivation of these transfer functions is one of the tasks of identification.

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

Johansson R: System Modeling and Identification. Prentice Hall, 1993. ISBN 0-13-482308-7.