Course Syllabus for

System Modeling and Identification Systemmodellering och -identifiering

FRT145F, 7.5 credits

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

General Information

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

Aim

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.

Goals

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

Course Contents

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

Course Literature

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

Instruction Details

Types of instruction: Lectures, laboratory exercises, exercises

Examination Details

Examination formats: Written exam, written assignments