# System Identification 2017 (News)

The schedule can be found here. The syllabus is here.

- We have the first lecture thursday 23-03, at 13.15-14.00 in room ITC1213.
- Since I found myself having a sore throat, lets cancel today's lecture and have the next one Wed. 26/05 at 1315-1500 instead.
- I need to have your computer lab and process lab reports before Monday 22e may.
- Computerlab 5 is revised (some of the reported links are broken it seems). The new version is here.
- The reports of the mini-project should be mailed to me the afternoon before the presentation (i.e. 23/05), so that I can read before the presentations.

# Material

- Slides: Slides 1, Slides 2, Slides 3, Slides 4, Slides 5, Slides 6, Slides 7, test_rls.m, Slides 8.
- Guest lecture Thomas Schön: slides (email: <thomas.schon@it.uu.se>), Erlendur Karlsson: slides (email:<erlendur.karlsson@ericsson.com>).
- Compendium: Lecture Notes
- Exercises: Help-slides, sol1, Help-slides
- Computer lab (see ch. 12 in the compendium): lab 1, lab 2
- Lab. session: Fan-lab, Eyetracking-lab,

# 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 Reglerteknik 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 some knowledge of linear algebra and statistical techniques.

Prerequisite are 120 ECTS credits and courses Signals and systems, Automatic control I, Automatic control II.

### 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 written exam (60%) and Part project work (40%).

The written exam will consist of 3 questions (exercises)

similar to the one given in the problem solving sessions.

The course counts for 5 ECTS

- Written exam: 3 ECTS.
- Mandatory labs: 1 ECTS.
- Project report and presentation: 1 ECTS.

### Lectures

There will be 9 lectures, each of 2 hours.

- Overview of the System Identification (SI) procedure.
- The Least Squares Estimator.
- Models & Representations.
- Stochastic Setup.
- Prediction Error Methods.
- Model Selection and Validation.
- Recursive Identification.
- On the transition from SISO to MIMO.
- Nonlinear System Identification.

### Exercise sessions

There will be

- 5 computer classes.
- Least Squares Estimation: do's and dont's.
- Timeseries Modeling.
- Recursive Identification.
- The System Identification Toolbox.
- MIMO: Kalman Filter and Subspace ID.

- 2 pen-and-paper exercise sessions:
- Aspects of Least Squares.
- Aspects of Recursive Least Squares.

- 1 laboratory session on the fan-lab.

### Course Material

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

- Lecture Notes and Slides (available in the course and on the website).
- Exercises
- Computer and laboratory lab instructions (available at every session).
- Project Manual (available on website).
- Additional reading: book 'System Identification, Söderström & Stoica which is available in electronic form at [1].

## 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 can be done in small groups of two persons, sharing the workload of the report and the presentations. The work will culminate in a report containing workflow, design decisions, details of the implementation, and results of simulations.

A successful project will consist of five steps, each of which are to be documented in the report.

- Visualize the data, point out characterizing properties and state the problem you're after.
- Do some simple (possibly naive) simulations: e.g. what is the best constant prediction (mean). This can often be done using the ident tool.
- 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)?
- 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?
- 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.
- 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 2-3 single column, 11pt, a4 pages.
- 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.

### Data

## Required Attendance and Homeworks

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

- A successful written exam, consisting of (1) multiple choice questions (2) one technical question asking for a derivation, and (3) the derivation of a recursive scheme.
- A project report (for a miniproject, the data is given, and a model should be build and be properly validated)..
- A successful presentation of the project (one per group of two persons).
- Attendance of the lab. session, as well as a filled out copy of the lab report.