Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
After completing the course, a student can select proper modeling approach for specific practical problems, formulate mathematical models of physical systems, construct models of systems using modeling tools such as MATLAB and Simulink, and estimate the parameters of linear and nonlinear static systems and linear dynamic systems from measurement data.
Schedule: 09.09.2020 - 08.12.2020
Teacher in charge (valid 01.08.2020-31.07.2022): Kai Zenger, Kai Zenger, Quan Zhou
Teacher in charge (applies in this implementation): Kai Zenger, Kai Zenger, Quan Zhou
Contact information for the course (applies in this implementation):
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Basic modeling methods, including first principle modeling and data-driven modeling, for both static and dynamic systems: first principle modeling, black box modeling, basics of regression methods, static parameter estimation for linear and non-linear systems, identification of linear time-invariant dynamical systems, model validation.
Assessment Methods and Criteria
The final grade will take into acount both the home assignments and the final exam. The actual distribution to be specified.
Lectures, exercise sessions, independent study and problem solving, home assignments.
Contact hours: 24 + 12 h
Independent study: 93 h
Handouts/lecture slides, Ljung, Modeling of dynamic systems, 1994, additional book chapters.
Programming in Matlab, Matrix and Linear Algebra, Basic course in Control Engineering or relevant knowledge.