Jedes Modul umfasst 3 ECTS. Sie wählen insgesamt 10 Module/30 ECTS in den folgenden Modulkategorien:
- 12-15 ECTS in Technisch-wissenschaftlichen Modulen (TSM)
TSM-Module vermitteln Ihnen profilspezifische Fachkompetenz und ergänzen die dezentralen Vertiefungsmodule. - 9-12 ECTS in Erweiterten theoretischen Grundlagen (FTP)
FTP-Module behandeln theoretische Grundlagen wie die höhere Mathematik, Physik, Informationstheorie, Chemie usw. Sie erweitern Ihre abstrakte, wissenschaftliche Tiefe und tragen dazu bei, den für die Innovation wichtigen Bogen zwischen Abstraktion und Anwendung spannen zu können. - 6-9 ECTS in Kontextmodulen (CM)
CM-Module vermitteln Ihnen Zusatzkompetenzen aus Bereichen wie Technologiemanagement, Betriebswirtschaft, Kommunikation, Projektmanagement, Patentrecht, Vertragsrecht usw.
In der Modulbeschreibung (siehe: Herunterladen der vollständigen Modulbeschreibung) finden Sie die kompletten Sprachangaben je Modul, unterteilt in die folgenden Kategorien:
- Unterricht
- Dokumentation
- Prüfung
Model Predictive Control (MPC) is an optimisation-based approach to control systems and processes. The general mathematical formulation of MPC allows it to be applied to a broad range of systems and considers system constraints intrinsically. The advances in optimisation methods and available computational power have made MPC a valuable alternative to classical control approaches also for fast dynamic systems. Today, MPC applications can be found from the original chemical process control systems to the control of frequency converters with sampling periods down to a few microseconds.
This module focuses on introducing MPC from the theoretical basics to the use of tool kits to support the implementation and generation of working code. As the classical frequency domain control methods are not considered here, this module does not need in-depth knowledge of control systems. A general affinity to mathematics and programming skills are beneficial.
Eintrittskompetenzen
- Linear Algebra
- Differential equations
- Basic feedback control and dynamic systems
- Basic programming skills in Matlab or Python or equivalent
- General affinity to mathematics(!)
Lernziele
The student is able to
- formulate an optimisation problem and solve it with appropriate tool kits
- formulate model predictive control problems
- apply MPC concepts to real world systems and generate executable code which runs on their control systems
Modulinhalt
Basic concepts ( 3W)
- Introduction to state space models in continuous and discrete time
- Introduction to optimisation (linear quadratic programs) using tool kits like YALMIP
- Introduction to optimisation with constraints
Basic MPC (3W)
- Linear MPC problem formulation
- Receding horizon concepts
- Limits of MPC
MPC Extensions and examples (5W)
- Reference tracking
- Error free tracking
- Nonlinear optimisation and MPC with nonlinear models
- Buck converter control (explicit MPC) -- optional
- Energy management (scheduling) -- optional
Real-time implementation(3W)
- From problem to code using tool kits like ACADO
Lehr- und Lernmethoden
Lectures with homework assignments which are a mix of theoretical exercises and programming assignments.
Vollständige Modulbeschreibung herunterladen
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