333 | Signal and system modelling | Physical engineering and embedded systems | S9 | ||||||
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Lessons : 8 h | TD : 4 h | TP : 15 h | Project : 0 h | Total : 27 h | |||||
Co-ordinator : Mohammed MSaad |
Prerequisite | |
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Signal processing Sampled feed-back systems Sampled linear systems Predictive control |
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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. |
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Syllabus | |
Motivations Identification models Least squares methods Parameter adaptation algorithms Predictive methods An experimental modeling predictive approach |
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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. |
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Acquired skills | |
An experimental approach for system (resp. signal) modeling for control system (resp. signal processing) design. |
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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 |
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