Identification of Dynamical Systems
lecturer: Ivan Markovsky, teaching assistant: Mehrad Sharabiany
Exercises
Reading materials
- I. Markovsky, Exact and approximate modeling in the behavioral setting
- Chapter 7: Introduction to dynamical models
- Chapter 8: Exact identification
- Chapter 11: Approximate system identification
- I. Markovsky. Low-Rank Approximation: Algorithms, Implementation, Applications
- Section 2.2: Dynamic model representations
- Chapter 3: Exact modeling
- Chapter 4: Approximate modeling
- I. Markovsky and F. Dörfler. Behavioral systems theory in data-driven analysis, signal processing, and control. Annual Reviews in Control, 2021
Mini-projects
Implementation of system identification methods
The objective is to implement and test subspace and maximum-likelihood system identification methods described in the course.
Benchmarking of system identification methods
The objective is to compare system identification methods from
- Identification Toolbox build in Matlab,
- IDENT package, and
- FRF toolbox
on the benchmark examples from the DAISY dataset.
For a similar comparison, see Section 8 in
An application of system identification in metrology
Extracting physical parameters from an identified black-box model
The identification methods taught in the course deliver "black-box" models, i.e., the models have no special structure. Structured models, also called "grey-box" and "white-box" models, may be interpretable. As an example consider a mass-spring-damper system. The identified model, given by a generic state-space or transfer function representation, does not make the physical parameter mass evident. The objective of the mini-project is to investigate this problem and propose possible solutions.
Reference:
Comparison of direct and indirect data-driven control
This mini-project goes beyond the material taught in the course. The objective is to reproduce the results in