Module RO4001-KP04

Duration

1 Semester

Turnus of offer

each winter semester

Credit points

4

Course of studies, specific fields and terms:

• Master MES 2020, optional subject, computer science / electrical engineering
• Master MES 2014, optional subject, computer science / electrical engineering

Classes and lectures:

• Model Predictive Control (exercise, 2 SWS)
• Model Predictive Control (lecture, 2 SWS)

Workload:

• 40 hours private studies
• 20 hours exam preparation
• 60 hours in-classroom work

Contents of teaching:

• LQ optimal control and Kalman filter
• Convex optimization
• Invariant sets
• Theory of Model Predictive Control (MPC)
• Algorithms for numerical optimization
• Explicit MPC
• Practical aspects (Robust MPC, Offset-free tracking, etc.)
• MPC applications

Qualification-goals/Competencies:

• Students get a comprehensive introduction to methods of optimal control.
• Students get an overview of the fundamentals of numerical optimization.
• Students are able to design model predictive controllers for linear and nonlinear systems.
• Students get acquainted with several tools to implement model predictive controllers.
• Students are able to establish system theoretic properties of model predictive controllers.
• Students gain insight into possible applications areas for MPC.

Grading through:

• Written or oral exam as announced by the examiner

Responsible for this module:

• Prof. Dr. Georg Schildbach

Teacher:

• Institute for Electrical Engineering in Medicine
• Prof. Dr. Georg Schildbach

Literature:

• F. Borrelli, A. Bemporad, M. Morari : Predictive Control for Linear and Hybrid Systems Cambridge University Press, 2017 (ISBN: 978-1107016880)

Language:

• offered only in English

Notes:

Last Updated:

07.10.2021