Course code: tn3111wb

Course name: System Identification

Course expired. See SC4110

This concerns a course 

ECTS credit points: 5

Faculty of Mechanical Engineering and Marine Technology

Section of

Lecturer(s): prof. dr. ir. P.M.J. Van den Hof and

dr. ir. X.J.A. Bombois

Tel.:  015 - 27 84509 / 85150

Catalog data:

Experimental modelling of dynamical systems; methodology. Discrete-time signals and system analysis. Identification of transfer functions. Representations of linear models; black-box models; parametrised model sets. Identification by prediction error minimization; least squares methods. Approximate modelling; algorithms. Experiment design and data analysis; model validation. MATLAB toolbox.    

Course year:

MSc 1^st year

Period:

2A / 2B

Hours per week:

4

Other hours:

-

Assessment:

Oral exam

Assessment period:

2B

(see academic calendar)

Prerequisites (course codes):

wb2310

Follow up (course codes):

--

Detailed description of topics:

• Methodology of identification; experimental modelling of dynamical systems; objectives and prospects.

• Discrete-time signals and systems.

• Identification of non-parametric models; transfer function estimation; spectral estimation.

• Prediction error identification of black box models; model structures; identification criteria; asymptotic properties; least squares methods; algorithmic aspects; approximate modelling.

• Model structure selection and model validation.

• Experiment design and data processing.

• MATLAB-toolbox

Course material:

• Lecture Notes: P.M.J. Van den Hof, "System Identification" (1998). (in English).

References from literature:

• L. Ljung, "System Identification - Theory for the User". 2^nd ed. Prentice Hall, Upper Saddle River, NJ, 1999
• T. Söderström and P. Stoica, "System Identification". Prentice Hall, Hemel Hempstead, UK, 1989.

Remarks assessment, entry requirements, etc.:

--

Learning goals:

This course is designed to provide the students with engineering knowledge and skills to design experiments, analyse data and to identify and validate models of dynamical systems, as appearing in industrial process control and mechanical servo systems. The methods as introduced in the course are supported by excercises on simulation and real-life examples, employing appropriate and user-friendly software tools.

Computer use:

Computers are used in the exercice sessions and for performing the take-home exam.

Laboratory project(s):

Design content:

The course is finished with an oral examination that is based on an assignment to identify and validate models from an experimental laboratory setup.

Percentage of design:  25%