Home - Log in

Signal and system modelling

333 Signal and system modelling Physical engineering and embedded systems S9
Lessons : 8 h TD : 4 h TP : 15 h Project : 0 h Total : 27 h
Co-ordinator : Mohammed MSaad
Prerequisite
Signal processing
Sampled feed-back systems
Sampled linear systems
Predictive control
Course Objectives
The fundamental features of system identification are comprehensively presented to provide a predictive experimental modeling approach to elaborate suitable design models for system control and signal processing. A particular emphasis is put on the system identification experimental planning, namely the input sequence choice, the data processing with respect to the considered identification method and the model validation process bearing in mind the required specification on the system to be designed together with any available prior knowledge on the signal or the system to be modeled.
Syllabus
Motivations
Identification models
Least squares methods
Parameter adaptation algorithms
Predictive methods
An experimental modeling predictive approach
Practical work (TD or TP)
A set of appropriate modeling problems is elaborated to develop the students'culture on system identification. A particular emphasis is put on the experimental planning, namely appropriate input sequence and data processing recovering thereby the required properties of a successful identification context. These modeling problems are studied using Matlab and Simulink environments.
Acquired skills
An experimental approach for system (resp. signal) modeling for control system (resp. signal processing) design.
Bibliography
I.D. Landau, R. Lozano and M. M'Saad (1997)
Adaptive Control
Springer , Communications and Control Engineering Series

L. Ljung (1987).
System Identification : Theory for the user.
Prentice-Hall, Inc.

M. M'Saad (2020)
Commannde predictive adaptative
Ouvrage d'automatique de l'Ecole Nationale Supérieure d'Ingénieurs de Caen

© 2024 - ENSICAEN ( Legal Notices - Credits )