Insegnamenti/Courses

 

IA/0117/EN – FAULT DIAGNOSIS AND ESTIMATION IN DYNAMICAL SYSTEMS

Academic year ​2020/2021

Lecturer   MAURO ​FRANCESCHELLI (Tit.)
Period   Second Semester ​
Modalità d’Erogazione    Convenzionale ​
Teaching languange    English ​

Course start: TBD

Course schedule: TBD

Additional information

Learning outcomes:

In observance with the teaching objectives of the master degree in Computer Engineering, Cybersecurity and Artificial Intelligence, the learning outcome of this course is to let the student obtain basic competences in relation to methods for state and exogenous inputs estimation in dynamical systems; as detailed in the following.

* Knolwedge and understanding:

The student will understand state variable models for the representation of dynamical systems through differential and difference equations and the structural properties of such systems.  The student will understand the most significant methods for state estimation and input reconstruction in dynamical systems, even accounting for some uncertainties in the dynamical models.

* Applying knowledge and understanding:

The student will know how to define the structural properties of a dynamical system. the student will know how to apply the fundamental methods for the design of state observers and unknown input observers (disturbances).

* Making judgements:

The student will be able to identify advantages and disadvantages of some observer designs.

* Communication:

The student will be able to describe with clarity technical and scientific concepts related to the estimation and diagnosis of dynamical systems.

* Lifelong learning skills:

The student will learn how to combine knowledge from various sources with the aim to achieve a wider understanding of the issues related to the design and implementation of systems for state estimation and diagnosis.

 

Prerequisites:

To follow the lectures with profit, the student is required to have the next competencies and skills:

Elements of mathematical analysis, matrix algebra and physics. Elements of analysis of linear dynamical systems. Laplace Transform. Integral and differential calculus. Elements of Matlab-Simulink programming.

 

Course contents:

Formal models of continuous-time and discrete-time dynamical systems

Analysis of dynamical systems

Structural properties of dynamical systems

State estimation and observers

Fault diagnosis

 

Readings/Bibliography :

Lecture slides

Katsuhiko Ogata, “Discrete-time control systems” second edition, Prentice Hall International editions, 1995

Alessandro GIUA, Carla SEATZU, Analisi dei sistemi dinamici- 2a edizione, Springer-Verlag Italia, MIlano, 2009.

Silvio Simani, Cesare Fantuzzi and Ron J. Patton “Model-based fault diagnosis in dynamic systems using identification techniques” Springer-Verlag 2002.

Hassan K. Khalil “Nonlinear systems” third edition, Pearson Eduction Limited 2014.

Jean-Jacques E. Slotine, Weiping Li “Applied Nonlinear Control” Prentice-Hall, 1991.

Other sources and papers provided during the lectures.

 

Teaching methods :

The course is taught with frontal lectures and exercitations which involve the use of software (Matlab) for numerical calculus and simulation of dynamical systems.

 

Assessment methods :

The achievement of the learning objectives is verified by an oral examination in which the students shows understanding of state variable models for the representation of dynamical systems, their properties and the most significant methods for diagnosis and estimation. The student will show autonomy in making judgments in regard to the design choices proposed in the course. The student will be able communicate with the appropriate technical language. During the oral examination, the student discusses the exercitations developed in the course.

The evaluation of the oral examination is quantified by a mark express in thirtieths.

The oral exam evaluates:

  1. The knowledge of the topics of the course (40% final mark)
  2. The application of the obtained knowledge to the design of observers (30% final mark)
  3. The autonomy in making judgments in regard to design choices (20% final mark)
  4. The use of technical language (10% final mark)


IA/0117/EN – FAULT DIAGNOSIS AND ESTIMATION IN DYNAMICAL SYSTEMS

Academic year ​2019/2020

Lecturer   MAURO ​FRANCESCHELLI (Tit.)
Period   Second Semester ​
Modalità d’Erogazione    Convenzionale ​
Teaching languange    English ​

 

Course start: Moonday 2nd March  in lab  LIDIA multifunzionale

Course schedule (subject to changes):

Moonday  11:00 a.m. – 13:00 a.n, Lab  LIDIA multifunzionale

Friday  09:00 p.m. -11:00 a.m.  I_1F (ex Aula I)

Additional information

 

Learning outcomes:

In observance with the teaching objectives of the master degree in Computer Engineering, Cybersecurity and Artificial Intelligence, the learning outcome of this course is to let the student obtain basic competences in relation to methods for state and exogenous inputs estimation in dynamical systems; as detailed in the following.

* Knolwedge and understanding:

The student will understand state variable models for the representation of dynamical systems through differential and difference equations and the structural properties of such systems.  The student will understand the most significant methods for state estimation and input reconstruction in dynamical systems, even accounting for some uncertainties in the dynamical models.

* Applying knowledge and understanding:

The student will know how to define the structural properties of a dynamical system. the student will know how to apply the fundamental methods for the design of state observers and unknown input observers (disturbances).

* Making judgements:

The student will be able to identify advantages and disadvantages of some observer designs.

* Communication:

The student will be able to describe with clarity technical and scientific concepts related to the estimation and diagnosis of dynamical systems.

* Lifelong learning skills:

The student will learn how to combine knowledge from various sources with the aim to achieve a wider understanding of the issues related to the design and implementation of systems for state estimation and diagnosis.

 

Prerequisites:

To follow the lectures with profit, the student is required to have the next competencies and skills:

Elements of mathematical analysis, matrix algebra and physics. Elements of analysis of linear dynamical systems. Laplace Transform. Integral and differential calculus. Elements of Matlab-Simulink programming.

 

Course contents:

Formal models of continuous-time and discrete-time dynamical systems

Analysis of dynamical systems

Structural properties of dynamical systems

State estimation and observers

Fault diagnosis

 

Readings/Bibliography :

Alessandro GIUA, Carla SEATZU, Analisi dei sistemi dinamici- 2a edizione, Springer-Verlag Italia, MIlano, 2009.

Katsuhiko Ogata, “Discrete-time control systems” second edition, Prentice Hall International editions, 1995

Silvio Simani, Cesare Fantuzzi and Ron J. Patton “Model-based fault diagnosis in dynamic systems using identification techniques” Springer-Verlag 2002.

Hassan K. Khalil “Nonlinear systems” third edition, Pearson Eduction Limited 2014.

Jean-Jacques E. Slotine, Weiping Li “Applied Nonlinear Control” Prentice-Hall, 1991.

Other sources and papers provided during the lectures.

 

Teaching methods :

The course is taught with frontal lectures and exercitations which involve the use of software for numerical calculus and simulation of dynamical systems.

The creation of study groups on specific topics will be evaluated during the lectures.

 

Assessment methods :

The achievement of the learning objectives is verified by an oral examination in which the students shows understanding of state variable models for the representation of dynamical systems, their properties and the most significant methods for diagnosis and estimation. The student will show autonomy in making judgments in regard to the design choices proposed in the course. The student will be able communicate with the appropriate technical language. During the oral examination, the student discusses the exercitations developed in the course.

The evaluation of the oral examination is quantified by a mark express in thirtieths.

The oral exam evaluates:

  1. The knowledge of the topics of the course (40% final mark)
  2. The application of the obtained knowledge to the design of observers (30% final mark)
  3. The autonomy in making judgments in regard to design choices (20% final mark)
  4. The use of technical language (10% final mark)

 


IA/0203/EN – CONTROL OF NETWORK SYSTEMS a.a. 2017/2018

Corso Percorso CFU Durata(h)
[70/83]  INGEGNERIA ELETTRONICA [83/00]  PERCORSO COMUNE 6 60

Periodo:  Secondo Semestre
Modalità d’erogazione: Convenzionale
Lingua Insegnamento: Inglese

 

Timetable for  a.y. 2017/2018

Moonday*   11:00 -13:00 AM  Room  Lidia-MF

Wednesday 08:00-10:00 AM Room Lidia-MF

Friday 08:00-10:00 AM Room R

*The lectures on moonday will be held only on even weeks, i.e., the second, the forth, and so on with respect to the first week of lectures.

First lecture  of  CNS a.y. 2017/2018: Wednesday  February 28th 18:00 PM, Room: Lidia-MF

Orario a.a. 2017/2018:

Lunedì* ore 11:00-13:00 Aula  Lidia-MF

Mercoledì ore 08:00-10:00 Aula Lidia-MF

Venerdì ore 08:00-10:00  Aula R

*Le lezioni del lunedì si terranno solo nelle settimane pari del corso, ovvero la seconda, la quarta, etc. rispetto all’inizio delle lezioni del secondo semestre della laurea magistrale in ingegneria elettronica.

Data inizio lezioni corso CNS a.a. 2017/2018: Mercoledì 28 Febbraio ore 18:00, Aula: Lidia-MF

 

Descrizione in italiano

Obiettivi

Questo corso è un modulo avanzato dell’Automatica che intende fornire una serie di metodi formali per la modellazione, l’analisi e il controllo di reti di sistemi dinamici, ovvero sistemi multi-agenti, composti da diverse entità dinamiche accoppiate o cooperanti attraverso una rete di comunicazione.

Il corso è incentrato sulla caratterizzazione del comportamento emergente in questo tipo di sistemi complessi, ovvero il comportamento collettivo o globale della rete generato da semplici regole di interazione tra i suoi componenti. Nel corso sono presentati una serie di scenari applicativi in cui gli strumenti formali presentati nel corso trovano come comune quadro di riferimento metodologico nei collegamenti tra la teoria algebrica dei grafi e l’automatica applicata all’analisi e al controllo di reti di sistemi quali le reti di sensori, i sistemi multi-robot, le reti sociali, la internet degli oggetti e le reti elettriche intelligenti.
Descrittori di Dublino
– Conoscenza e capacità di comprensione relative alle reti di sistemi dinamici e agli strumenti formali usati per descriverli.
– Conoscenza e capacità di comprensione applicate che consentano di risolvere problemi originali di modellazione e analisi delle reti di sistemi dinamici.
– Autonomia di giudizio: gestione della complessità di una rete di sistemi, astraendo il suo comportamento mediante un modello matematico, e capacità di progetto e simulazione al calcolatore di algoritmi distribuiti.
– Abilità comunicative: descrizione in modo preciso e non ambiguo del comportamento dinamico di una rete di sistemi e di eventuali specifiche sul suo comportamento emergente dal punto di vista matematico e ingegneristico.
– Capacità di apprendere: essere in grado di proseguire lo studio delle reti di sistemi identificando i metodi formali opportuni per le applicazioni di interesse.

Prerequisiti
Le conoscenze impartite nei corsi di matematica e di automatica della laurea triennale.
Conoscenza dei metodi di analisi dei sistemi dinamici tramite modelli ingresso/uscita e in variabili di stato.

 

English description

Objectives

This is an advanced course in the field of control theory which deals with formal methods for the modelling, analysis and control of network systems, composed by coupled dynamical entities cooperating through a communication network.

The course is focused on the characterization of the emerging behavior of these complex systems, i.e., the global or collective behavior of the network system caused by simple and local interaction rules among its components. In this course several application scenarios are presented, the analysis of these scenarios finds a common methodological framework  in the connection between algebraic graph theory and control theory applied to the study of network systems such as sensor networks, multi-robot systems, social networks, the internet of things and smart grids.

Dublin descriptors:

– Knowledge and understanding of networks of dynamical systems and of formal tools used to describe them.
– Applying knowledge and understanding to solve original problems concerning modeling and analysis of network systems.
– Making judgements: how to manage the complexity of a network of systems by abstracting its behavior by means of a mathematical model and the ability to design and simulate distributed control algorithms.
– Communication skills: precise specification of the emergent behavior of network system from the mathematical and engineering point of view.
– Learning skills: ability to undertake further autonomous studies on the theory of network systems by identifying the state of the art tools most suitable for the applications of interest.

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