Nonlinear Model Predictive Control
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of WS 2013/4
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
|available module versions|
|SS 2014||WS 2013/4|
MA5321 is a semester module in English language at Master’s level which is offered irregular.
This module description is valid from WS 2013/4 to SS 2017.
|Total workload||Contact hours||Credits (ECTS)|
|180 h||60 h||6 CP|
Content, Learning Outcome and Preconditions
Stability of nonlinear differential equations, Ljapunov functions, asymptotic controllability, different methods of feedback stabilization, stability and stabilization of perturbed systems
Nonlinear Model predictive control (NMPC):
Fundamentals of NMPC, finite and infinite horizon NMPC, stability and suboptimality with/without constraints, feasibility and robustness
MA2304 - Numerical Methods for Ordinary Differential Equations
MA2005 - Ordinary Differential Equations
MA2503 - Nichtlineare Optimierung
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VO||4||Nonlinear Model Predictive Control||Callies, R.||
Fri, 10:15–11:45, MI 03.06.011
Mon, 14:15–15:45, MI 02.08.011
Learning and Teaching Methods
In the lectures, the contents will be presented in a talk with demonstrative examples, as well as through discussion with the students. The lectures should motivate the students to carry out their own analysis of the themes presented and to independently study the relevant literature. Corresponding to each lecture, practice sessions will be offered, in which exercise sheets and solutions will be available. In this way, students can deepen their understanding of the methods and concepts taught in the lectures and independently check their progress.
E. Camacho, C. Bordons: Model predictive control, Springer 2008
J. Rawlings: Model predictive control, Nob Hill Pub 2009
Description of exams and course work
The exam may be repeated at the end of the semester.