**Lecturer: Lucian Busoniu, TAs: Mihalis Maer, Matthias Rosynski, Bilal Yousuf**

Navigation: [Versiunea romana|Evaluation rules|Schedule|Lectures|Labs|Lab tests|Project|Contact] [Back to Lucian Busoniu's webpage]

The course introduces nonparametric methods for system identification, as well as parametric methods including prediction error and instrumental variables techniques. Input signals, online recursive methods, closed-loop identification, and model validation are also discussed. The material is described at an appropriate BSc level, and builds in a self-contained manner the required mathematical background. The course is based on the book *System Identification* by Soderstrom and Stoica.

This year, we will use a mix of offline teaching and online platforms: Microsoft Teams for interacting with students, and ClassMarker to take the quizzes. As a unique ID for each student, please use an email address that is associated to a ClassMarker account. Microsoft Teams can be associated with the different, Didatec email address. Details on how we are using these platforms can be found under the specific type of activity below.

This course is part of the Bachelor program of the Automation Department, UTCluj (3rd year 1st semester). Prerequisites: linear dynamical systems and linear algebra. The lecturer is Lucian Busoniu. Mihalis Maer and Lucian teach the projects, while Matthias Rosynski and Bilal Yousuf teach the lab classes.

Grading:

- 30%=2x15% two lab tests, see their section below for details.
- 10% lab quizzes: a short quiz with two questions, at the start of each lab except the first, from the material relevant to that lab.
- 30% final exam.
- 30% project (15% part 1 + 15% part 2), see below for details.
- 10% lecture quizzes: short quizzes during each lecture, from the material discussed during that particular lecture.

Eligibility conditions: Solutions to all labs except the first one; the two lab tests; and solutions to both parts of the project are all required before being admitted to the exam. More details about each component (labs, lab tests, project evaluation) will be found at the appropriate places in their separate descriptions below.

You can check your current status in the Google sheet table here, updated in near-real-time.

Lectures are Wednesdays from 8AM, labs and projects on Mondays and Tuesdays. Please look at the schedule below carefully to determine exactly when you should be in class, and see the overall schedule on http://www.aut.utcluj.ro/ for when your time slots are allocated.

Due to interdependencies between lectures, labs, and project classes; as well as lab tests and project deadlines, the actual schedule is slightly different from the official one. The following changes are performed:

- Since we need lecture 2 for lab 2, and labs come before lectures, we postpone lab 2 to week 3.
- In week 2 we have 2 lectures, and in week 3 you will take control engineering instead of sysid.
- During lab test weeks, we may need to use project slots for the test as well, so that everyone has time to take the test.
- Since week 10 has two national holidays, we will simply skip the entire week, as recovering from 2 missing days in a coherent way is nearly impossible.
- For project session 4, the even-week halfgroups take the project in an odd week (number 9). The labels "odd" and "even" in the table refer to the time-slots in which you would come according to the official schedule.
- The last lecture slot is a backup for project presentations that do not fit in the regular project slots of the previous week.

The lecture slides are mandatory reading; they will be written down in detail to give a self-contained, complete picture of the topics. They are made available here in time for each lecture.

- Part 1: Introduction to System Identification (PDF).
- Part 2: Transient Analysis of Step and Impulse Responses (PDF).
- Part 3: Mathematical Background: Linear Regression and Statistics (PDF).
- Part 4: Correlation Analysis (PDF).
- Part 5: ARX Identification (PDF).
- Part 6: Input Signals (PDF).
- Part 7: General Prediction Error, ARMAX, and OE Methods (PDF).
- Part 8: Instrumental Variable Methods. Closed-Loop Identification (PDF).
- Part 9: Recursive Identification (PDF).
- Part 10: Model Validation and Practical Issues (PDF).

At a random point during a lecture, a short quiz will be given from the material of that lecture, using the ClassMarker platform. A list of email addresses will be collected beforehand and you will be invited to ClassMarker. The quizzes are graded! So, you should pay attention during the lectures. If you answer all the questions in every lecture correctly, you get 1 point in your grade; lower scores scale linearly to between 0 and 1 points in the grade.

In addition to the slides, followers may optionally consult the following books:

- T. Soderstrom and P. Stoica.
*System Identification*. Prentice Hall, 1989. The full text of this book is available at: http://user.it.uu.se/~ts/bookinfo.html. This book forms the basis of the course. - L. Ljung,
*System Identification: Theory for the User*, 2nd ed., Prentice Hall, 1999. An advanced graduate-level textbook.

Labs will be developed using Matlab. PDF descriptions of the labs are given on this website.

Lab solutions are required starting from lab 2. It is not enough to be present at the lab in order to validate it; a complete, working, and original solution must be developed during the lab. Whether the solution is **working** will be verified by the teacher during the lab. Once this has been verified, you submit the solution via a dropbox link. **Originality** will then be verified as described below. At most two labs total can be recovered before the exam, including at most one copied lab; hence, accumulating three or more missing labs means you can no longer become eligibile for the exam.

Half-group boundaries are strict, for example you cannot access the quiz to the lab unless you are present during the lab slot of your halfgroup.

Each lab except the first starts with a 2-minute quiz containing 2 short questions, which tests you on the material relevant to that particular lab. If you answer everything correctly for all the labs, you get 1 point in your grade; lower scores scale linearly, as for the lecture quizzes. So, you should arrive prepared.

The solutions to each lab are run through an automated plagiarism check. Each suspicious case is examined by the teachers, and if it becomes clear that the lab is copied, both the source and the destination lab are invalidated. In addition, we are imposing a "two strikes - you're out" rule. Namely, the first lab copied is only invalidated, and you can recover it at the end. The second lab copied invalidates the full set of solutions, you forfeit the discipline and have to take it next year.

- Lab 1: (Re)Introduction to Matlab (PDF) -- two short exercises in order to get re-acquainted with Matlab. For a brief intro to Matlab, have a look at the following document (with thanks to Paula Raica for allowing us to use it) ST_Lab1.pdf. You may also use the automated Matlab tutorial, with thanks to Tassos Natsakis who developed it.
- Lab 2: Transient Analysis of Step Responses (PDF). In case you want to solve the assignment in regular Matlab as well (not just in Grader), the data files are: for first-order systems, #1, #2, #3, #4, #5, #6, #7, #8 (where # stands for index). For second-order systems, #1, #2, #3, #4, #5, #6, #7, #8.
- Lab 3: Transient Analysis of Impulse Responses (PDF). The data files are: for first-order systems, #1, #2, #3, #4, #5, #6, #7, #8. For second-order systems, #1, #2, #3, #4, #5, #6, #7, #8.
- Lab 4: Linear Regression for Function Approximation (PDF). The data files are: #1, #2, #3, #4, #5, #6, #7, #8, #9, #10, #11, #12, #13, #14, #15, #16.
- Lab 5: Correlation analysis (PDF). The data files are: #1, #2, #3, #4, #5, #6, #7, #8.
- Lab 6: ARX Identification (PDF). The data files are: #1, #2, #3, #4, #5, #6, #7, #8.
- Lab 7: Pseudo-Random Binary Sequences (PDF). Download the system simulator: system_simulator.p, and the validation input: uval.mat.
- Lab 8: Identification of OE models with the Gauss-Newton method (PDF). The data files are: #1, #2, #3, #4, #5, #6, #7, #8.
- Lab 9: Instrumental Variable Methods (PDF). The data files are: #1, #2, #3, #4, #5, #6, #7, #8.
- Lab 10: Recursive Identification (PDF). The data files are: #1, #2, #3, #4, #5, #6, #7, #8.

**Lab test 1:** The slots will be assigned in the online table. Please check carefully as due to scheduling constraints some students are out of their regular slot (reassignments are done preventing overlaps with other activities). This is mainly because there are a lot of students and delays after a half-group's lab slot are likely. Keep in mind that the lab tests are required for eligibility. The test will have three components, described and graded as follows:

- 3 points: a ClassMarker quiz during the lecture on November 10th, from the material relevant to the test. So you have to attend the lecture, otherwise, you will miss these points.
- 5 points: code for redoing a (part of a) lab between 2 and 5, i.e., transient analysis, linear regression, or correlation analysis.
- 2 points: one-on-one question(s), both on the code and testing the higher-level insight about the methods in the labs. Bonus points are possible if you demonstrate that you understand the topics significantly above the expected level.

The grade at each lab test is added with a 0.15 weight to the final grade, irrespective of its value.

See the project description (PDF) for the topic, rules, and deadlines. Please read it carefully. A detailed description of the linear regression method needed in the first part of the project can be found in last year's lecture; we will talk about it in lecture 4. Alternately, here is a one-page (handwritten) summary.

Guidelines for writing your report and developing and giving your presentation are also available.

Comments, suggestions, questions etc. related to this course or website are welcome; please contact either the lecturer or the TAs via Teams.