Linear Control

5.00 credits

30.0 h + 30.0 h

Teacher(s)

Dochain Denis;

Language

English

Prerequisites

Notions of signals and systems as taught in LEPL1106.

Main themes

Development of mathematical models for linear dynamical systems (state-space representation, transfer functions) allowing to represent the dynamics in a unified way for a diversity of engineering applications (e.g. electromechanical, mechanical, electrical, chemical, biological, computer science)
Design of control schemes that meet specifications related to stability, transient and steady state performance (accuracy), and robustness. PI and PID controllers, Linear Quadratic Control, Smith predictor, feedforward control, cascade control. Use of software to design controllers.

Learning outcomes

At the end of this learning unit, the student is able to :

With respect to the referentiel AA, this courses contributes to the development, the acquisition and the evaluation of the following learning outcomes :

AA1.1, AA1.2, AA1.3
AA5.3, AA5.4, AA5.5

At the end of the course, the student will be able :

Design control systems based on linear models;
Design of control schemes that meet specifications on related to stability, transient and steady state performance (accuracy), and robustness. PI and PID regulators, Linear Quadratic Control, Smith predictors, feedforward control, cascade control;
Use software to design controllers.;
Implement closed-loop control system in laboratory experiments under conditions similar to those in industrial applications.;
Use industrial PID controller;
Autonomously run automatic control experiments, from the design level to the actual implementation and performance evaluations;

Content

Mathematical Models
General principles of closed-loop systems and control
Stability
Steady state accuracy
Disturbance rejection
Performance in transient regime
Robustness
Controller structures and anti-windup
Case studies: electrical systems, mechanical systems, automobile, aeronautics, thermal and nuclear powerplants, heat exchanger, industrial grinding and mixing processes, (bio)chemical processes, distillation columns, biomedical applications, electronics and telecommunication, etc.

Teaching methods

Problem-based learning, laboratory experiments. The course will be given either in presence mode or in distance mode.

Evaluation methods

Laboratory evaluation outside of the exam period and written exam, either under the format of an oral evaluation or via the use of an evaluation software for the laboratory evluation, either under an hand-written mode or via the use of an evaltaion software for the written exam. The teacher reserve the right to examine orally any student besides the laboratory evaluation and the written exam.

Online resources

https://moodleucl.uclouvain.be/course/view.php?id=7834

Bibliography

Transparents de théorie, notices de laboratoire et d'exercices, fiches, fichiers d'exemples et d'llustration des concepts.
Livre de référence : K. Astrom & R. Murray, Feedback Systems: An Introduction for Scientists and Engineers http://www.cds.caltech.edu/~murray/amwiki/index.php

Faculty or entity

MAP

Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme

Sigle

Credits

Prerequisites

Learning outcomes

Master [120] in Mechanical Engineering

MECA2M

Master [120] in Chemical and Materials Engineering

KIMA2M

Master [120] in Electrical Engineering

ELEC2M

Minor in Engineering Sciences: Applied Mathematics (only available for reenrolment)

MINMAP

Master [120] in Electro-mechanical Engineering

ELME2M

Specialization track in Biomedical Engineering

FILGBIO

Minor in Applied Mathematics

LMINOMAP

Specialization track in Applied Mathematics

FILMAP