First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
AUTOMATION ENGINEERING
Course unit
NETWORKED CONTROL FOR MULTI-AGENT SYSTEMS
INP7079117, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course Second cycle degree in
AUTOMATION ENGINEERING
IN0527, Degree course structure A.Y. 2008/09, A.Y. 2018/19
N0
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Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination NETWORKED CONTROL FOR MULTI-AGENT SYSTEMS
Department of reference Department of Information Engineering
E-Learning website https://elearning.dei.unipd.it/course/view.php?idnumber=2018-IN0527-000ZZ-2017-INP7079117-N0
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge ANGELO CENEDESE ING-INF/04

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses ING-INF/04 Automatics 9.0

Course unit organization
Period First semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 9.0 72 153.0 No turn

Calendar
Start of activities 01/10/2018
End of activities 18/01/2019

Examination board
Board From To Members of the board
1 A.A. 2018/2019 01/10/2018 15/03/2020 CENEDESE ANGELO (Presidente)
ZORZI MATTIA (Membro Effettivo)
BEGHI ALESSANDRO (Supplente)
BISIACCO MAURO (Supplente)
CARLI RUGGERO (Supplente)
CHIUSO ALESSANDRO (Supplente)
FERRANTE AUGUSTO (Supplente)
PILLONETTO GIANLUIGI (Supplente)
PINZONI STEFANO (Supplente)
SCHENATO LUCA (Supplente)
SUSTO GIAN ANTONIO (Supplente)
TICOZZI FRANCESCO (Supplente)
VALCHER MARIA ELENA (Supplente)
ZAMPIERI SANDRO (Supplente)

Syllabus
Prerequisites: To attend the Networked Control for Multi-agent Systems classes the following knowledge is required:
- Linear algebra (vector spaces, matrix operations),
- Control/system theory (state space systems, structural properties),
- Data analysis (random variables, linear regression),
- Filtering and estimation (stochastic systems, statistical filtering)
Target skills and knowledge: The learning objectives of the classes are:
- get the knowledge of advanced tools for dynamical system modeling, estimation and control, specifically with reference to multi-agent systems (e.g. robotic and camera networks)
- develop the ability to manage the scientific tools and knowledge of the whole master programme to solve an engineering/research problem
- develop the ability to formalize a problem and devise a strategy to address its solution
- develop the ability to organize the teamwork within a project task
- develop the ability of producing technical reports and present a technical project
Examination methods: The assessment of the expected competence acquisition is carried out via:
1- Written Homework in groups made of 3-4 people: an implementation homework related to optimization algorithm will be assigned
2- Intermediate individual Classtest: this test will concern the theoretical material taught in class
3- Development of a Project in groups made of 3-4 people: projects starting from application problems are assigned and have to be solved with respect to the theoretical/algorithmic/simulation issues. The project deliveries are:
-a- Written report: technical report on the assigned problem and the proposed solutions
-b- Oral presentation and discussion: slide presentation of the project and discussion over the proposed solutions

Final grading is obtained by combining the single grading contributions as follows:
1(10%) + 2(30%) + 3a(30%) + 3b(30%)
1 and 2 can be replaced with an oral examination on the related topics.
Assessment criteria: The evaluation criteria for the expected acquired competences regard:
- technical knowledge of the main methodologies of modeling, analysis, and synthesis for networked control systems, in particular with reference to robotic networks, camera networks, sensor networks
- ability to formalize a specific engineering problem, solve it by exploiting the tools and techniques acquired during the laurea curriculum,
- ability to discuss the design choices​​, both qualitatively and quantitatively
- ability to technically present the project results, both orally and through a written report, both qualitatively and quantitatively
Course unit contents: Advanced topics for modeling, estimation, control
- Optimization: Singular Value Decomposition, Least Square techniques, Optimization problems, gradient-based methods
- Multiagent systems: pose representation, camera networks, robotic networks, unmanned vehicle systems
- Consensus theory on graphs: graph theory, Perron-Frobenius theory, consensus algorithms
- Applications: multi view geometry, pose reconstruction, distributed calibration, localization, coverage
Planned learning activities and teaching methods: The teaching activities include:
- class lectures
- matlab laboratory
- industrial seminars
- periodic meeting activities for the project development
Additional notes about suggested reading: The teaching material includes:
- Lecture notes organized as a booklet.
- Book chapters and other handouts related to specific topics.
- Papers from the scientific literature.

All material is made available on the e-learning system.
Textbooks (and optional supplementary readings)
  • Dimitri P. Bertsekas, Nonlinear Programming. --: Athena Scientific, 2nd edition, 1999. Cerca nel catalogo
  • Dimitri P. Bertsekas and John N. Tsitsiklis, Parallel and Distributed Computation: Numerical Methods (Optimization and Neural Computation). --: Athena Scientific, 1 edition, 1997. Cerca nel catalogo
  • Stephen Boyd and Lieven Vandenberghe, Convex Optimization. --: Cambridge University Press, 2004. Cerca nel catalogo
  • Hamid Aghajan and Andrea Cavallaro, Multi-Camera Networks principles and applications. --: Elsevier, 2009. Cerca nel catalogo
  • Yi Ma, Stefano Soatto, Jana Kosecka, and Shankar S. Sastry, An Invitation to 3-D Vision: From Images to Geometric Models (Interdisciplinary Applied Mathematics). --: Springer, 2005. Cerca nel catalogo
  • Mehran Mesbahi and Magnus Egerstedt, Graph Theoretic Methods in Multiagent Networks. --: Princeton University Press, 2010. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Laboratory
  • Case study
  • Working in group
  • Problem solving
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Latex
  • Matlab

Sustainable Development Goals (SDGs)
Industry, Innovation and Infrastructure