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Lecture

Distributed Computation: Consensus Algorithms in Networked Control Systems

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 application of consensus algorithms in networked control systems, focusing on topics such as Metropolis-Hasting models, weighted adjacency matrices, node counting, and distributed computation of Least-Squares regression. The instructor explains how to compute BLUE in a distributed way, the consensus on vector-valued and matrix-valued quantities, and the distributed computation of LS regression. The lecture also delves into the explicit formulas for LS, the goal of making each node compute LS with a distributed algorithm, and the time-varying consensus algorithms' robustness to changes in graph topology, lossy communication, transmission delays, and quantization.

Instructor

Giancarlo Ferrari Trecate

Official source

Ontological neighbourhood

Mathematics

Discrete mathematics: Graph theory

Statistics

Data analysis: Regression analysis

Related concepts (32)

Industrial control system

An industrial control system (ICS) is an electronic control system and associated instrumentation used for industrial process control. Control systems can range in size from a few modular panel-mounted controllers to large interconnected and interactive distributed control systems (DCSs) with many thousands of field connections. Control systems receive data from remote sensors measuring process variables (PVs), compare the collected data with desired setpoints (SPs), and derive command functions that are used to control a process through the final control elements (FCEs), such as control valves.

Control system

A control system manages, commands, directs, or regulates the behavior of other devices or systems using control loops. It can range from a single home heating controller using a thermostat controlling a domestic boiler to large industrial control systems which are used for controlling processes or machines. The control systems are designed via control engineering process. For continuously modulated control, a feedback controller is used to automatically control a process or operation.

Adjacency matrix

In graph theory and computer science, an adjacency matrix is a square matrix used to represent a finite graph. The elements of the matrix indicate whether pairs of vertices are adjacent or not in the graph. In the special case of a finite simple graph, the adjacency matrix is a (0,1)-matrix with zeros on its diagonal. If the graph is undirected (i.e. all of its edges are bidirectional), the adjacency matrix is symmetric. The relationship between a graph and the eigenvalues and eigenvectors of its adjacency matrix is studied in spectral graph theory.

Ordinary least squares

In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values of the variable being observed) in the input dataset and the output of the (linear) function of the independent variable.

Distributed control system

A distributed control system (DCS) is a computerised control system for a process or plant usually with many control loops, in which autonomous controllers are distributed throughout the system, but there is no central operator supervisory control. This is in contrast to systems that use centralized controllers; either discrete controllers located at a central control room or within a central computer. The DCS concept increases reliability and reduces installation costs by localising control functions near the process plant, with remote monitoring and supervision.

Related lectures (35)

Distributed Algorithms for Networked Control Systems

Explores distributed algorithms for networked control systems, covering consensus, least-squares regression, and time-varying communication networks.

Consensus Algorithms: Weight Assignment and Applications

Explores the design of graph weights for consensus and applications in sensor networks.

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: Laplacian Matrix and Consensus

Explores the Laplacian matrix and consensus in networked control systems.

Linear Regression: Maximum Likelihood Approach