Lecture

Consensus Theorem for Communication Networks

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In course

ME-427: Networked control systems

This course offers an introduction to control systems using communication networks for interfacing sensors, actuators, controllers, and processes. Challenges due to network non-idealities and opportunities offered by communication will be analyzed. ...

Description

This lecture covers the consensus theorem for communication networks that are not fully connected, providing examples and summarizing the results discussed so far. The instructor explains the concept of condensation digraphs and consensus, illustrating how states evolve autonomously and influence others over time. The lecture delves into the main result of consensus with GR nodes, emphasizing the importance of primitive and stochastic matrices. Generalizations to multiple subgraphs of leaders are also explored, along with properties of adjacency matrices and initial states. The implications of various consensus properties are discussed, highlighting the interconnected nature of networked control systems.

Instructor

Giancarlo Ferrari Trecate

Expertise Automatic control, Dependable control, Neural-network Control, Machine learning, Microgrids, Hybrid systems Mission See the webpage of the DECODE (Dependable Control and Decision) group. Teaching & PhD PhD Students Laura Meroi, Julien Pallage, Nicolas Léo Pierre Kirsch, Simone Baratto, Daniele Martinelli, Riccardo Cescon, Jakob Nylöf, Sabri El Amrani Past EPFL PhD Students Pulkit Nahata, Mustafa Sahin Turan, Baiwei Guo, Jean-Sébastien Brouillon, Clara Galimberti, Andrea Martin, Muhammad Zakwan,

Official source

Related concepts (31)

Adjacency list

In graph theory and computer science, an adjacency list is a collection of unordered lists used to represent a finite graph. Each unordered list within an adjacency list describes the set of neighbors of a particular vertex in the graph. This is one of several commonly used representations of graphs for use in computer programs. An adjacency list representation for a graph associates each vertex in the graph with the collection of its neighbouring vertices or edges.

Logical matrix

A logical matrix, binary matrix, relation matrix, Boolean matrix, or (0, 1)-matrix is a matrix with entries from the Boolean domain B = {0, 1}. Such a matrix can be used to represent a binary relation between a pair of finite sets. It is an important tool in combinatorial mathematics and theoretical computer science.

Orthostochastic matrix

In mathematics, an orthostochastic matrix is a doubly stochastic matrix whose entries are the squares of the absolute values of the entries of some orthogonal matrix. The detailed definition is as follows. A square matrix B of size n is doubly stochastic (or bistochastic) if all its rows and columns sum to 1 and all its entries are nonnegative real numbers.

Stochastic matrix

In mathematics, a stochastic matrix is a square matrix used to describe the transitions of a Markov chain. Each of its entries is a nonnegative real number representing a probability. It is also called a probability matrix, transition matrix, substitution matrix, or Markov matrix. The stochastic matrix was first developed by Andrey Markov at the beginning of the 20th century, and has found use throughout a wide variety of scientific fields, including probability theory, statistics, mathematical finance and linear algebra, as well as computer science and population genetics.

Networked control system

A networked control system (NCS) is a control system wherein the control loops are closed through a communication network. The defining feature of an NCS is that control and feedback signals are exchanged among the system's components in the form of information packages through a network. The functionality of a typical NCS is established by the use of four basic elements: Sensors, to acquire information, Controllers, to provide decision and commands, Actuators, to perform the control commands and Communication network, to enable exchange of information.

Related lectures (58)

Consensus in Networked Control Systems

Explores consensus in networked control systems through graph weight design and matrix properties.

Consensus with GR Nodes

Explores consensus with GR nodes in networked control systems, focusing on condensation digraphs and the main result.

Time-Varying Consensus Algorithms

Explores time-varying consensus algorithms in networked control systems and the Laplacian matrix's role in achieving average consensus.

Networked Control Systems: Properties and Connectivity

Explores properties of matrices, irreducibility, and graph connectivity in networked control systems.

Networked Control Systems