System identification

Summary

Identification of discrete-time linear models using experimental data is studied. The correlation method and spectral analysis are used to identify nonparametric models and the subspace and prediction error methods to estimate the plant and noise model parameters. Hands-on labs are included.

Content

Keywords

System identification, spectral analysis, correlation approach, prediction error method

Learning Prerequisites

Recommended courses

Dynamic systems, Control systems

Important concepts to start the course

• Represent a physical process as a system with its input, outputs and disturbances
• Analyze a linear dynamical system (both time and frequency response)
• Represent a linear system by a transfer function (discrete- and continuous-time)

Learning Outcomes

By the end of the course, the student must be able to:

• Identify a dynamic system using experimental data, A6
• Construct and analyze a discrete-time model for a dynamic system, A5

Transversal skills

• Write a scientific or technical report.
• Plan and carry out activities in a way which makes optimal use of available time and other resources.
• Set objectives and design an action plan to reach those objectives.

Teaching methods

Ex-cathedra course with hands-on labs and project

Expected student activities

Hands-on laboratory for groups of two students, preparing technical reports.

Assessment methods

Written test (70%) and lab reports (30%).

Supervision

Resources

Notes/Handbook

Course-notes (in English): System Identification

Slides available (pdf) in English

Moodle Link

• https://go.epfl.ch/ME-421