Systemmodellering och -identifiering

**Valid from:** Autumn 2016**Decided by:** Professor Thomas Johansson**Date of establishment:** 2016-10-27

**Division:** Automatic Control**Course type:** Course given jointly for second and third cycle**The course is also given at second-cycle level with course code:** FRT041**Teaching language:** English

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 doctoral student must

- 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

*Competences and Skills*

For a passing grade the doctoral student must

- 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

*Judgement and Approach*

For a passing grade the doctoral student must

- 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: 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 0134823087.

**Types of instruction:** Lectures, laboratory exercises, exercises

**Examination formats:** Written exam, written assignments**Grading scale:** Failed, pass**Examiner:**

**Assumed prior knowledge:** FRT010 Automatic Control, Basic Course, FMSF10 Stationary Stochastic Processes

**Course coordinator:** Rolf Johansson `<rolf.johansson@control.lth.se>`**Web page:** https://www.control.lth.se/Education/EngineeringProgram/FRT041.html