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COURSE SYLLABUS

System identification 7.5 credits

Systemidentifiering
Second cycle, R7015E
Version
Course syllabus valid: Autumn 2021 Sp 1 - Present
The version indicates the term and period for which this course syllabus is valid. The most recent version of the course syllabus is shown first.


Education level
Second cycle
Grade scale
G U 3 4 5
Subject
Control Engineering
Subject group (SCB)
Automation Technology
Main field of study
Engineering Physics and Electrical Engineering

Entry requirements

Knowledge in the subject of Automatic control, specifically regarding frequency response, state-space form, and state feedback as well as some experience of using Matlab for analysis of control systems. This prerequisite knowledge corresponds to the course R7003E Automatic Control. Some basic level knowledge about probability and statistics, e.g. from the course S0008M Probability Theory and Statistics, is also recommended. Good knowledge in English equivalent to English 6.


More information about English language requirements


Selection

The selection is based on 20-285 credits



Course Aim
The student should be able to:
  • formulate and implement algorithms for system identification, i.e. estimation of mathematical models of a dynamic system from input-output data
  • formulate and implement algorithms for state estimation, i.e. to infer the status of the internal variables of a dynamic system using measurements of other quantities and some knowledge of the system dynamics
  • solve simple instances of system identification and estimation problems by hand
  • analyze and prove properties of system identification and estimation algorithms
  • apply the above described techniques on real-world processes, and report on this work both orally and in writing.

Contents
The course covers the essentials of two interconnected topics: system identification and state estimation.

System identification is the science dealing with how to model systems starting from collected evidence. Among the statistical sciences, this branch is the one most related to automatic control. Indeed developing a control system usually starts with a system identification step: there is a process to be controlled, but there is either no model for it, or an incomplete model where some parameters are unknown, or maybe there is a model, but it is too complicated for developing a controller (for example a finite element simulator of the thermal dynamics of a whole datacenter, and you want to control the temperature of the racks).

State estimation is instead dealing with reconstructing information on the state of a system starting usually from indirect measurements. For example, gyroscopes are usually subject to some bias, but this bias cannot be measured directly. It is nonetheless possible to infer it indirectly combining knowledge of the dynamics of the system and measurements from the sensors. This state information is then useful for performing automatic control tasks, e.g., feedback from the state.


Realization
Each course occasion´s language and form is stated and appear on the course page on Luleå University of Technology's website.
The teaching consists of lectures and problem seminars.
Lab work is performed in groups of no more than two students and accounted for with written reports and a demonstration.

Examination
If there is a decision on special educational support, in accordance with the Guideline Student's rights and obligations at Luleå University of Technology, an adapted or alternative form of examination can be provided.
Written examination with differentiated grades and approved lab work. 

Examiner
Thomas Gustafsson

Transition terms
The course R7015E is equal to R7011E

Literature. Valid from Autumn 2021 Sp 1 (May change until 10 weeks before course start)
Title: Modelling and Identification of Dynamic Systems
Author: L. Ljung; T. Glad
Publisher: Studentlitteratur, 2019
ISBN: 9789144116884

Course offered by
Department of Computer Science, Electrical and Space Engineering

Modules
CodeDescriptionGrade scaleHPStatusFrom periodTitle
0001Laboratory workU G#3.00MandatoryA18
0003Written examG U 3 4 54.50MandatoryA21

Study guidance
Study guidance for the course is to be found in our learning platform Canvas before the course starts. Students applying for single subject courses get more information in the Welcome letter. You will find the learning platform via My LTU.

Syllabus established
by Jonny Johansson, HUL SRT 15 Feb 2018

Last revised
by Jonny Johansson, HUL SRT 16 Feb 2021