Search    Print

Modellistica e Identificazione dei Processi Dinamici
Modeling and identification of dynamic processes
Anna Maria Perdon

Seat Ingegneria
A.A. 2015/2016
Credits 9
Hours 72
Period II
Language ENG

Prerequisites
The student should have a good knowledge of the notions provided by basic courses in Mathematics and in Automatic Control. It 'also useful to know how to use programming tools

Learning outcomes
The aim of the course is to provide the student theoretical and practical knowledge of methods to develop mathematical models from experimental data. He will acquire the basics on building models for input-output linear dynamic systems, on prediction error methods for parameter estimation and the skills to practically apply the methods and techniques learned.

Program
Introduction and generalities about model construction and systems identification from experimental data. Models and parametric identification. Data collection and related problems. Best model and identification techniques (LS, ML, recursive methods). Model validation. Systems in state space form. Structural properties and relations between state space representations and external I/O representations. Realization of transfer function in state space form. Generalities on Neural Networks. An outline of identification by Neural Networks. Implementation of identification methods by Matlab System Identification Toolbox. Lab with NI myDAQ, a low-cost portable data acquisition (DAQ) device that uses NI LabVIEW-based software instruments.

Development of the examination
LEARNING EVALUATION METHODS
The learning evaluation will consist of a written test consisting consists of four questions of a theoretical nature, on the topics discussed in class and contained in the materials provided to the students. Each student must also complete a practical project on one of the topics discussed in class and present a report on this activity. The project can also be done in groups , with a maximum of three students . In this case , the discussion of the project must take place with the participation of all students in the context of the same group. In the case of a negative result of one of the tests, the student can repeat only that part, provided this is done within the same academic year.

LEARNING EVALUATION CRITERIA
Correctness, completeness and clarity in answering the questions in the theory test. Accuracy and completeness in solving the exercises. As for the project, the student must prove that he can apply the concepts learned in the course, to properly use the tools and appropriate technologies and to write a clear technical report.

LEARNING MEASUREMENT CRITERIA
The written test consists of 4 groups of questions on the various parts of the program, each group contains a question which is assigned a score between 0 and 10, and a question which is assigned a score between 0 and 6. The student must answer a question in each group, choosing two questions for 10 points and two for 6 points. The test is considered “sufficient” if the score is greater or equal to 15. The practical project is assigned a score from 0 to 30 and is “sufficient” only if the score is greater or equal to 18.

FINAL MARK ALLOCATION CRITERIA
The overall grade is given by the arithmetic mean, rounded up to the whole, of the sum of the scores obtained in the written test and in the project if both are sufficient. The overall grade required to pass the exam is 18 points. Otherwise the overall grade is “Not sufficient” . The student who in addition to getting a score greater than or equal to 30 has demonstrated complete mastery of the topics addressed, and clarity of exposition will have a “30 e lode”.

Recommended reading
Identificazione dei Modelli e Controllo Adattativi, S. Bittanti – Pitagora Editrice Bologna Slides and exercises can be found on the web site http://leibniz.diiga.univpm.it/~perdon/didattica/Modellistica.html

Courses

• Ingegneria Informatica e dell'Automazione (Corso di Laurea Triennale (DM 270/04))