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FRTN15 - Predictive Control

Prediktiv reglering, 7.5 hp

Syllabus CEQ Schedule

PLEASE NOTE: This course is replaced by FRTN75 Learning-Based Control from 2022

Frequently asked questions are collected here.

Instructors 2020


Problem solving sessions and labs

  • Marcus Greiff <>
  • Julian Salt <>

Recommended Prerequisites:

Automatic Control (FRT010), some background in discrete-time signals and systems.


Course Material


Lectures will be held in M:E or M:B on Tuesdays 13.15–15.00 or 15.15-17.00 and Thursdays 13.15–15.00 or 15.15-17.00 according to the schedule:

421/1L1Introduction. Signals & Systems. Real-time Parameter Estimation.Ch. 1, 2, 10
 23/1L2Automatic Tuning, Gain Scheduling, Auto-calibration. Ch. 1, 2, 3
528/1L3ARMAX models. Pole assignment Model Matching. Optimal Control. Ch. 5
 30/1L4Pole Assignment. Model matching. Disturbance Models. LQ Control. Ch. 5, 9
 64/2L5Optimal Prediction. Optimal Predictive Control. The Kalman Filter. LQG Control. Ch. 5, 6, 7
 6/2L6Adaptive Control Ch. 11
 711/2L7Adaptive Control. Ch. 11
 13/2L8Model Predictive Control (MPC) Ch. 16
818/2L9Iterative Learning Control (ILC). Iterative Feedback Tuning (IFT). Ch. 15
 20/2L10More Model Predictive Control Ch. 16
925/2L11Stability: Lyapunov Theory. App.  B
 27/2L12Stability: Input-Output Stability. Passivity. App. C
103/3L13Stochastic Adaptive Control Ch. 12, 13
 5/3L14Implementation. Applications. Summary. Hour for Questions.


Copies of the lecture slides are available here (the username is "control", you'll also need the fun password mentioned at the lecture).

Some Matlab Code etc

  • ex0.m (Stochastic system, Lecture 2)
  • ex1.m (Correlation and Covariance, Lecture 2)
  • design1.m (Polynomial design Lecture 4)
  • predictor.m (Optimal d-step aheap prediction, Lecture 4)
  • (All matlab code used in Lecture 5, incl the useful rstd.m and dab.m)
  • (All matlab code used in Lecture 6)
  • (All matlab code used in Lecture 7)
  • (All matlab code used in Lecture 8)
  • MPC TOOLS manual (used in Lecture 10)
  • MPC Tools (including the Quad tank and Helicopter examples in Lecture 10)
  • (ILC code used in Lecture 11)


LP3 Fridays 10.15-12.00 in E:1147:

The recommended reading below should be seen as a guide. Generally you will have to read a bit more outside of the recommended reading in order to grasp the concepts, but it will be a good place to start. Johansson refers to the "Lecture notes: Predictive and Adaptive Control" (2020 edition) and Åström refers to the book "Adaptive Control" by Karl-Johan Åström (2009 edition).

 Week (Exercise)
 DateRecommended ReadingContents
4 (E1)29/1

(i) Johansson(This is not covered in the book of Johansson, but the manual should be sufficient); (ii) Åström Section 5.1-5.5; (iii) Manual for computer exercise.

Simulation of Adaptive Systems. Sign up here!
5 (E2)31/1(i) JohanssonSections 10.1-10.2; (ii) Åström, Sections 2.1-2.5 and 11.5 (be sure to understand Theorem 2.3-2.4 and examples 2.2-2.8)Real-Time Parameter Estimation.
6 (E3)7/2(i) Johansson Sections 4.1-4.3 and 7.4; (ii) Åström Section 4.2 (The Kalman filter is not addressed in the book of Åström)Optimal Prediction. Optimal estimation. Kalman filter.
7 (E4)14/2(i) Johansson Sections 5.1-5.4; (ii) Åström Section 3.1-3.2 and 3.4-3.6Adaptive Control.
8 (E5)21/2(i) Johansson Sections 16.1-16.3; (ii) Åström (MPC is not addressed in the book of Åström)Model Predictive Control
9 (E6)28/2(i) Johansson Sections 15.1-16.4; (ii) Åström (ILC is not addressed in the book of Åström)Iterative Learning Control (ILC)
106/3All of the above, including lab manuals and the computer exercise manual.Exam questions

Exercise Materials

    1. Exercise 1 with Notice that this computer exercise is held in Lab B, please sign up in advance.
    2. Exercise 2 and solutions
    3. Exercise 3 and solutions
    4. Exercise 4 and solutions
    5. Exercise 5 and solutions
    6. Exercise 6 and solutions
    7. Exercise 7: Old Exams (see below) and Questions

Laboratory Sessions

Sign up for the laboratory sessions here, and do so at least one day in advance. Note that Lab 1 is now updated, and there is no preparations needed apart from reading the relevant chapters in the course book.

Home Work Assignents

Please send your solutions by the deadlines to the emails by the specified deadline. Solutions should be submitted in individual short reports written in a PDF format. Reasonable cooperation is allowed (but not copying other persons solutions).

Please send your solutions by the deadlines to the emails mentioned in the handins. You will get feedback on your solution, and if it needs revision, you will have to submit a revised solution within a week of receiving the feedback. 


Tuesday, March 17, at 14-19 in MA:10A-B. Some old exams are given below.


The projects will be done individually or in small groups of 2-4 students. A list of project proposal can be found in the project list. You should sign up for a project no later than Sunday, March 1. The deadline for the project report is April 29 unless otherwise agreed with the project supervisors. The project should be presented Wednesday, April 29, at 10:15-12:00 in the seminar room M:2112B of Dept. Automatic Control. All project groups should give an oral presentation of 5-10 minutes. Presence mandatory for students of FRTN15. WELCOME!

Project Groups 2020

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