Predictive Control
Prediktiv reglering

FRTN15F, 7.5 credits

Valid from: Spring 2017
Decided by: Professor Thomas Johansson
Date of establishment: 2016-10-27

General Information

Division: Automatic Control
Course type: Course given jointly for second and third cycle
The course is also given at second-cycle level with course code: FRTN15
Teaching language: English

Aim

The aim of the course is to provide advanced knowledge and skills about design of control systems that include predictive, adaptive and learning algorithms for control of time-variable and partially unknown processes with disturbances, including stability and interaction between control and identification.

Goals

Knowledge and Understanding

For a passing grade the doctoral student must

• be able to define the basic concepts for systems with muliple input and output signals
• be able to translate between different multivariable system descriptions, in particular transient responses, transfer function matrices, and state-space descriptions
• be able to compute the properties of interconnected systems from the properties of the subparts
• be able to show how limitations in process knowledge put limitations on the achievable control performance
• understand possibilities and limitations in the use of adaptation and learning

Competences and Skills

For a passing grade the doctoral student must

• be able to formulate control specifications for predictive control
• be able to translate control specifications for model-based control problems
• be able to draw conclusions from the results of predictive control about model plausibility and specifications

Judgement and Approach

For a passing grade the doctoral student must

• understand relations and limitations when simplified models are used to describe complex and multivariable systems
• show ability for teamwork and group collaboration during laboratory experiments and projects

Course Contents

Real-time identification, Recursive identification, Automatic controller tuning, Gain scheduling, Automatic calibration, Discrete-time linear systems, Pole-placement, Model reference system, Disturbance models, Optimal prediction, Optimal model-based predictive control, Adaptive control, Self-tuning control, Stochastic adaptive control, Model reference adaptive control, Stability, Passivity, Robustness, Model-predictive control, Iterative learning control, Iterative controller tuning, Applications and software

Course Literature

Johansson, R.: Predictive and Adaptive Control. Department of Automatic Control, LTH, 2010.

Instruction Details

Types of instruction: Lectures, laboratory exercises, exercises, project

Examination Details

Examination formats: Written exam, written assignments. Assessment: Written exam, project, three laboratory exercises, two hand-in problems.
Grading scale: Failed, pass
Examiner:

Admission Details

Course Occasion Information

Contact and Other Information

Course coordinator: Rolf Johansson <rolf.johansson@control.lth.se>
Web page: http://www.control.lth.se/Education/EngineeringProgram/FRTN15.html