# 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

#### Lectures:

- Rolf Johansson <Rolf.Johansson@control.lth.se>, tel. 046-222 8791, office M:5147).

#### Problem solving sessions and labs

- Marcus Greiff <Marcus.Greiff@control.lth.se>
- Julian Salt <Julian.Salt_Ducaju_at_control.lth.se>

#### Recommended Prerequisites:

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

#### Course Material

- Course Program 2020
- Lecture notes: Predictive and Adaptive Control, 2020 (R. Johansson) is available through KFS.
- Additional reading for interested: K J Åström and B Wittenmark, Adaptive Control, Second Edition, Dover 2008
- Lab manuals, exercises and solutions are available below.

#### Lectures

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:

Week | Date | No | Contents | Ref. |

4 | 21/1 | L1 | Introduction. Signals & Systems. Real-time Parameter Estimation. | Ch. 1, 2, 10 |

23/1 | L2 | Automatic Tuning, Gain Scheduling, Auto-calibration. | Ch. 1, 2, 3 | |

5 | 28/1 | L3 | ARMAX models. Pole assignment Model Matching. Optimal Control. | Ch. 5 |

30/1 | L4 | Pole Assignment. Model matching. Disturbance Models. LQ Control. | Ch. 5, 9 | |

6 | 4/2 | L5 | Optimal Prediction. Optimal Predictive Control. The Kalman Filter. LQG Control. | Ch. 5, 6, 7 |

6/2 | L6 | Adaptive Control | Ch. 11 | |

7 | 11/2 | L7 | Adaptive Control. | Ch. 11 |

13/2 | L8 | Model Predictive Control (MPC) | Ch. 16 | |

8 | 18/2 | L9 | Iterative Learning Control (ILC). Iterative Feedback Tuning (IFT). | Ch. 15 |

20/2 | L10 | More Model Predictive Control | Ch. 16 | |

9 | 25/2 | L11 | Stability: Lyapunov Theory. | App. B |

27/2 | L12 | Stability: Input-Output Stability. Passivity. | App. C | |

10 | 3/3 | L13 | Stochastic Adaptive Control | Ch. 12, 13 |

5/3 | L14 | Implementation. 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)
- Lec5.zip (All matlab code used in Lecture 5, incl the useful rstd.m and dab.m)
- Lec6.zip (All matlab code used in Lecture 6)
- Lec7.zip (All matlab code used in Lecture 7)
- Lec8.zip (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)
- Lec11.zip (ILC code used in Lecture 11)

### Exercises

LP3 Fridays 10.15-12.00 in E:1147:

- Marcus Greiff <Marcus.Greiff@control.lth.se>
- Julian Salt <Julian.Salt_Ducaju_at_control.lth.se>

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

**refers to the book "Adaptive Control" by Karl-Johan Åström (2009 edition).**

*Åström* Week (Exercise) | Date | Recommended Reading | Contents |

4 (E1) | 29/1 | (i) 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) Sections 4.1-4.3 and 7.4; (ii) JohanssonSection 4.2 (The Kalman filter is not addressed in the book of Åström)Åström | Optimal Prediction. Optimal estimation. Kalman filter. |

7 (E4) | 14/2 | (i) Sections 5.1-5.4; (ii) JohanssonSection 3.1-3.2 and 3.4-3.6Åström | Adaptive Control. |

8 (E5) | 21/2 | (i) Sections 16.1-16.3; (ii) Johansson(MPC is not addressed in the book of Åström)Åström | Model Predictive Control |

9 (E6) | 28/2 | (i) Sections 15.1-16.4; (ii) Johansson(ILC is not addressed in the book of Åström)Åström | Iterative Learning Control (ILC) |

10 | 6/3 | All of the above, including lab manuals and the computer exercise manual. | Exam questions |

#### Exercise Materials

- Exercise 1 with exercise_1_public.zip. Notice that this computer exercise is held in Lab B, please sign up in advance.
- Exercise 2 and solutions
- Exercise 3 and solutions
- Exercise 4 and solutions
- Exercise 5 and solutions
- Exercise 6 and solutions
- 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.

- Lab 1 - Gain scheduling, Lab files. Done in week 6. Responsible: Marcus Greiff
- Lab 2 - Adaptive Control, Lab files, Preparation files. Done in week 8. Responsible: Marcus Greiff
- Lab 3 - Predictive Control, Lab files, MPC Tools. Done in week 9. Responsible: Julian Salt

### 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).

- Home Work 1—Signals and Systems. Supplementary materials: Sampling Notes. Deadline February 9, 2020. Responsible: Marcus Greiff
- Home Work 2—Adaptive Control. Supplementary materials: Software Tools, Adaptlib Notes, Sampling Notes. Deadline February 16, 2020. Responsible: Marcus Greiff
- Home Work 3—Model Predictive Control (MPC). Supplementary materials: Software Tools. Deadline February 23, 2019. Responsible: Julian Salt

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.

### Exam

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

### Projects

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!