News

(1 Feb. 2011) - Dear, I was expecting to have a lecture at 1pm today (1 feb), but the schedule learned me wrong. I will have the lecture next thursday (3 feb., 13:15-15:00 , System Identification, Pol_1213) rather than the exercise session.
My sincere apologies once more,
Kristiaan

(15 Feb. 2011) - I need to have a printed, named report on the 4 lab sessions from each of you at the last lecture (next Thursday, 24. Feb, 12am.). I will give you feedback on those in the days following that one by email. There is no separate report needed for exercises as indicated before, but make sure you understand the exercises given in the sessions. The solutions to the exercise sessions will be available online by the end of next week.

(23 Feb. 2011) - as there appeared to be a dead link on the website, I believe it's fair enough for me to extend the deadline for the reports of the computer labs to next Monday (28 Feb. 2011, before 5pm). Can you leave the reports then in hardcopy at my office, or hand them in to my neighbors (Egi or Marcus).

(28 Feb. 2011) - I have to cancel tomorrows' laboratory session as there seems to be a conflict on the schedule. I will have the solutions to exercise session 2 as Darine gave them online tomorrow (on PEM), and have two simple exercises worked out which cover the exercise session 2 (that was planned) concerning least squares estimation.

(1 March) Concerning the exam (as mentioned during lectures): you are allowed to use any course material; i.e. slides, lecture notes, exercises (+solutions) and book. You are not allowed to use external aids like laptops, scientific calculators or alike.

(22 March) Dear all. There appeared once more a flaw in the schedule. The lectures are only to be given in April and the dates are now fixed on the schedule. I will post the project this Thursday morning, together with instructions on how to do the initial analysis and to decide on the small groups for a certain project.

(31 March) The project descriptions and data are online. Do walk through the description, pick a project and do ask a fellow students if they want to join in a group (1-5) persons. Then do send your group composition (the students involved) as well as the project you target to me by email, and I will record those at studentportalen.

(3 Mai) The Process labs are now scheduled in 2 groups. The first group (10 students) will be scheduled next Thu. 12 Mai at 3pm, the other group will be scheduled the week after (19 Mai) at 2pm. The lab takes place in room 2314. Please sign up in studentportalen when you would like to take the lab. I expect a filled in copy of the report/manual from each of you.

(18 Mai) I will collect the reports for the exams at 9am, Monday 23 Mai from the postbox on the second floor. By exception I allow e-mails with e-copies. The list of who/where is online at studentportalen. The presentations will take place in Polacksbacken, house 2, room 2345. The reports are to be in the postbox 26 at house 2, floor 2.

Info

(This info can be found in overview_2011.pdf. In case of questions send an email to )

• Lecture Notes: Lecture Notes
• Lecture slides: Part I: Lecture 1, Lecture 2, Lecture 3, Lecture 4, Lecture 5, Lecture 6, Lecture 7; Part II: Lecture 8, Lecture 9, Lecture 10, Lecture 11, Lecture 12
• Exercise sessions: Ex.1 + solutions, Ex.2 + solutions, Ex.3 + solutions, Ex.4 + solutions
• Computer Labs: lab 1, lab 2, lab 3, lab 4, Feedback, lab 5
• MATLAB code: lab 1, lab 2, lab 3, lab 4, lab 5
• projects: The description is found here, the data of the projects is found in prj1, prj2, prj3, prj4.

System Identification

Many engineering tasks start from an appropriate mathematical model of the studied system. This is the case in many simulation, control, prediction or monitoring applications. This course teaches you how such models can be obtained based on measurements collected from, and experiments carried out with the studied system. Both theoretical issues and practical case studies are used to provide insight in this fascinating research area.
Follow this course if:
(i) You want to learn how to set up a successful identification experiment of a dynamical system.
(ii) How can you verify the use of your estimated models.
(iii) What are key concepts lying on the basis of a statistical analysis of such techniques?
(iv) What are the different steps in making the techniques 'work' on a real case?

• Requirements: 120 ECTS credits and passing courses on Reglerteknisk I & II, Signals and Systems.
• Level of Education: Advanced Level.
• Grading System: U Fail, 3 Pass, 4 Pass with Credit, 5 Pass with Distinction.
• Main Area of Studies: Technology.

Prerequisites

This course builds on a reasonable knowledge of linear algebra and statistical techniques.
Prerequisite are 120 ECTS credits and courses Signals and systems, Automatic control I, Automatic control II.

Required Attendance and Homeworks

In order to pass the course, I need to have for each of the candidates:

1. A project report.
2. A successful presentation of the project (possibly shared amongst partners in the group).
3. A report with solutions for the exercises appointed to working out at home.
4. Attendance of the lab. session, as well as a filled out copy of the lab report.
5. A filled out report of the computer sessions.

Desiderata

Students who pass the course should be able to

• Describe the different phases that constitute the process of building models, from design of an identification experiments to model validation.
• Explain why different system identification methods and model structures are necessary in engineering practice.
• Account for and apply the stochastic concepts used in analysis of system identification methods.
• Describe and motivate basic properties of identification methods like the least-squares method, the prediction error method, the instrumental variable method, as well as to solve different problems that illustrate these properties.
• Explain the advantages and challenges when identifying feedback systems in closed loop.
• Describe the principles behind recursive identification and its field of application.
• Explain the usefulness of realization theory in the context of system identification, and how it is employed in subspace identification techniques.
• Show hands-on experience with analyzing actual data, and have a working knowledge of the available tools. Reason about how to choose identification methods and model structures for real-life problems.

Examination

Part project work (50%) and part written exam (50%).
The written exam will consist of 5 questions (exercises)
similar to the one given in the problem solving sessions.
The course counts for 10 ECTS

• Part I (written exam): 5 ECTS.
• Mandatory homeworks and labs: 1 ECTS.
• Projects: 4 ECTS.

Lectures

There will be 12 lectures, each of 2 hours.

Part I: Basic

1. Overview, Aim and Relations of System Identification.
2. Least Squares Rules.
3. Model representations: Non-parametric & Parametric approaches.
4. Stochastic Setup.
5. Prediction Error Method.
6. Model Selection and Validation.
7. Recursive Identification.

Part II: Advanced

1. State Space Systems.
2. Subspace Systems.
3. Further Topics.
4. Nonlinear Identification: a flash forward.
5. Views and Perspectives.

Course Material

Most material, as well as a message board is available at

[1]

1. Lecture Notes and Slides (available on website).
2. Book 'System Identification, S"oderstr"om & Stoica which is available at UTH-gard. The book is available in electronic form at [2].
3. Exercise Compendium (available on website).
4. Computer and laboratory lab instructions (available at every session).
5. Project Manual (available on website).

Problem Solving Sessions

1. Aspects of Least Squares.
2. Statistical Aspects: what can go wrong with OLS?
3. Prediction Error Methods.
4. Recursive Identification.

At the end of each problem solving session, an exercise is given to the students
which has to be solved at home. Those 4 exercises+solutions are to be handed in at the last session.

Computer Labs

1. Least Squares Estimation: do's and don'ts.
2. Timeseries Modeling.
3. Recursive Identification.
4. The System Identification Toolbox.
5. MIMO: Kalman Filter and Subspace ID.

Laboratory Work: Identification of a fan process

Project Works

General Instructions

The aim is to acquire hands-on experience with the tools for system identification for working with actual (real-life) data. Specifically, use of the MATLAB SI toolbox, and learning to organize different available tools in a successful application are central. The emphasis will be on identification of Multiple-Input, Multiple-Output (MIMO) systems. The projects will be organized in small groups (depending on the number of participants to the course), with indication of individual contribution. The work will culminate in a report containing workflow, design decisions, details of the implementation, and results of simulations. It is allowed and encouraged to work in small groups (2-5 persons)
on the same task. But it is required that each individual hands in her/his own report (named).
A successful project will consist of five steps, each of which are to be documented in the report.

1. Visualize the data, point out characterizing properties and state the problem you're after.
2. Do some simple (possibly naive) simulations: e.g. what is the best constant prediction (mean). This can often be done using the ident tool.
3. What is a proper method for identification of the system, perform the simulations. Most importantly, verify the result: why is this result satisfactory? How does it compare to the naive estimates of (2)?
4. Describe a full identification experiment: why is this (not) possible in practice? What would be the benefit if it were possible? What are further important todo's?
5. Summarize your contribution in an 'abstract' and 'conclusions' of your report. Those different steps (sections) should show up in the report to be handed in.
6. Report: A well-manicured report describing the achieved results, motivating the design decisions and verifying the estimated models. Make sure sufficient care is given to (a) Avoid Typos. (b) Use of the English language: think about what you write and how you write it up. (c) Be concise: reread your own text and throw out what is not needed for supporting the conclusions. (d) Figures: name axes, and give units. Add a legend explaining the curves we see, and add a caption explaining what we see and should conclude from the present figure. (e) A guideline would be a report of 3-4 single column, 11pt, letter pages.
7. Presentation: each group is assigned a slot of 10 minutes to defend their results. Specifically, try to convince the audience of the following bullets: (a) What are the conclusions of the effort, and how do you get there?(b) How do you improve over earlier/simpler solutions? (c) What is the contribution of each of the groupmembers? (d) What are possible applications for your work? (e) Suppose I were your manager at a company: why should I invest 1000\$ to implement your model? (f) Suppose I were your teacher: why would I award you a grade 5 for your work?

After and during the presentation, I will ask some questions for each of you evaluating your
insights in SI as used in the project.

Projects:

1. Identification of an industrial Petrochemical plant.
2. Identification of an Acoustic Impulse Response.
3. Identification of Financial Stock Markets.
4. Identification of a Multimedia stream.