- Docente: Giuseppe Notarstefano
- Credits: 8
- SSD: ING-INF/04
- Language: English
- Moduli: Giuseppe Notarstefano (Modulo 1) Giuseppe Notarstefano (Modulo 2)
- Teaching Mode: Traditional lectures (Modulo 1) Traditional lectures (Modulo 2)
- Campus: Bologna
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Corso:
Second cycle degree programme (LM) in
Computer Engineering (cod. 0937)
Also valid for Second cycle degree programme (LM) in Automation Engineering (cod. 8891)
Learning outcomes
The course provides the basic principles for distributed control systems, both functionally and architecturally. The main topics are basic principles of decentralized and distributed control, consensus algorithms and their application to synchronization and coordination problems, control of homogeneous multi-agent systems, estimation and filtering in distributed systems environment, characteristics of HW/SW architectures for real-time distributed systems, the role of digital networks in real-time systems, synchronization issues and time management in distributed systems, interaction of real-time processes in distributed systems. At the end of the course students have a deep knowledge of the problems regarding distributed systems and of the tools to develop control and estimation solution in distributed environments.
Course contents
New paradigms and applications of autonomous systems: smart cyber-physical network systems. Introduction to distributed (control) systems: centralized versus distributed approaches. Examples of distributed systems (e.g., sensor networks, cooperative robots and social networks). Key properties and main goals for distributed systems.
Graph theory as a tool to model communication among agents. Preliminaries on graph theory. Matrices associated to graphs.
Distributed algorithms and distributed control laws. Averaging protocols and linear consensus algorithms for discrete-time and continuous-time multi-agent systems. Complex tasks (e.g., formation control, containment) based on linear consensus algorithms.
Introduction to distributed optimization: main problem set-ups and examples from estimation, learning, decision and control problems in cyber-physical networks.
Basics of constrained optimization theory: optimality conditions, main iterative algorithms, duality.
Distributed optimization algorithms based on average consensus. Decomposition schemes for distributed optimization.
Software tools for distributed control and optimization in cyber-physical networks: applications to complex autonomous systems (e.g., smart energy systems), machine learning and cooperative robotics.
Readings/Bibliography
The course is based on the books
“F. Bullo, Lectures on Network Systems”
“D. Bertsekas, Nonlinear Programming”
and a set of articles/notes which will be made available throughout the term.
Teaching methods
Traditional lectures at the board and lab exercising
Assessment methods
Oral exam and discussion of a course project.
Teaching tools
"Virtuale" (course content and material, useful info). Software for the simulation of distributed control and optimization algorithms in distributed control systems and robotic networks.
Office hours
See the website of Giuseppe Notarstefano