The VUB-ELEC department was proud to organize the 2017 edition of the workshop on System Identification (a biennial event), as a continuation of 9 successful editions of the ELEC Doctoral School on the Identification of Nonlinear Dynamic Systems (with a slightly altered format). In Spring 2019 (May 27-June 21) the second edition will take place.
System identification is the engineering discipline which aims at constructing mathematical models of physical systems, based on measured data. The purpose of these estimated models can be very broad: to gain physical insights into the system, to build a robust controller, to detect anomalies, to compensate for unwanted behaviour, to predict future outcomes, etc... Since 1989, the VUB-ELEC department has gathered a lot of experience in system identification.
The ELEC workshop on System Identification brings together researchers in System Identification and researchers/industrials who are in need of modelling tools based on measured data. As a particpant, you will have the opportunity to learn from, and collaborate with experts in (frequency domain) system identification to solve your own modelling problem.
The previous years, the doctoral school has been glad to host participants from 23 different countries across the world, in application areas including robotics and automation, electrochemical impedance spectroscopy, bio mechanics, telecommunication, ...
Example application areas
System identification has applications in a lot of engineering disciplines. Some examples below.
Electrochemical impedance spectroscopy
This includes the detection and characterisation of corrosion of metals, and the characterisation of the state-of-charge (SOC) and state-of-health (SOH) of batteries.
Heavy duty electrical machines involve the use of highly nonlinear electronic components in complex control loops. Reliable models of these components are crucial for the predictability and thus the safety of these devices.
(HF/RF) electronic circuits
The requirements on (HF/RF) electronic circuits like amplifiers and mixers is becoming increasingly stringent, such that advanced modelling and identification tools are required for design and distortion compensation purposes.
In robotics, a position dependency of the dynamic behaviour can be observed, which can be captured by using nonlinear or time-varying identification tools. In the study of structural vibrations (modal analysis), the modelling tools must take into account the possible variations of the load (think of a bridge with a variable density of cars driving on top, or a wind turbine subject to different wind speeds).
Making black box models of (parts of) living creatures is very challenging, and involves the appearance of many nonlinear and parameter-varying effects. Think of the influence of moisture on the mechanical and electrical properties of biological tissues, or the dependence of the dynamics of joints on the muscular activity.
Courses lectured (with exercises)
During the first two weeks of the workshop, the following topics will be covered during the lectures and exercises.
Frequency response function measurements (Non-parametric tools)
In this course we learn how the frequency response function (FRF) of a linear dynamic system is measured using periodic and random excitations. The attendees will learn how to
- design broadband periodic excitations (multisines)
- make FRF-measurements using periodic excitations and random excitations: how to select the power spectrum? how to deal with transients? How to measure the noise characteristics? How to tackle a situation in feedback? How to work in an errors-in-variables framework?
- how to design excitation signals for detecting and quantifying nonlinear distortions and time variations?
- how to distinguish the nonlinear distortions and the time variations from the noise contributions from a single experiment?
Dynamic System Identification (Parametric tools)
In this course we learn how to build mathematical models starting from noisy data or measurements. Then, we apply this methodology to the parametric identification of linear, nonlinear, and time-varying dynamic systems. The following topics are covered:
- Why do we need identification methods? A simple example.
- Properties of estimators: asymptotic unbiased and consistent estimators; efficiency; Cramér-Rao lower bound.
- A systematic approach to the identification problem: least squares, weighted least squares, Maximum likelihood, Bayes' estimators.
- Model selection and validation criteria.
- Formulate estimators for dynamic systems, with a specific attention to the assumptions on the setup (Zero-Order-Hold versus Band-limited), also tackling the problem of missing data.
- Behavioural theory appplied to system identification, with solution methods in the low rank approximation setting (more info).
- The parametric identification of time- and parameter varying systems.