Lecture

Discrete-Time Control Systems: Implementation

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In course

ME-321: Control systems + TP

Provides the students with basic notions and tools for the analysis and control of dynamic systems. Shows them how to design controllers and analyze the performance of controlled systems. ...

Description

This lecture covers the concept of discrete-time control systems and their implementation using digital controllers. It explains how sensors and computers perceive the world at fixed time intervals, and how control actions are updated in discrete-time. The lecture also discusses the perspective of the system and the controller in continuous and discrete-time domains, as well as the differences between continuous-time and discrete-time signals. Additionally, it delves into sampling techniques, the Nyquist Theorem for selecting sampling rates, and the importance of sampling frequency in capturing system dynamics.

Instructors (2)

Christophe Salzmann

Expertise Real-time Internet ServicesReal-time controlQuality of Service and Bandwidth adaptationLabVIEW Education PhD | Remote experimentation, Network 2005 – 2005 EPFL Directed by Denis Gillet MS. Degree | Computer Science 1999 – 1999 University of Florida Directed by Prof. H. Latchman Teaching & PhD Past EPFL PhD Students as codirector Damien Perritaz, Evgeny Bogdanov, Wissam Halimi Courses Control systems + TP ME

Colin Neil Jones

Colin Jones is an Associate Professor in the Automatic Control Laboratory at the Ecole Polytechnique Federale de Lausanne (EPFL) in Switzerland. He was a Senior Researcher at the Automatic Control Lab at ETH Zurich until 2011 and obtained a PhD in 2005 from the University of Cambridge for his work on polyhedral computational methods for constrained control. Prior to that, he was at the University of British Columbia in Canada, where he took a BASc and MASc in Electrical Engineering and Mathematics. Colin has worked in a variety of industrial roles, ranging from commercial building control to the development of custom optimization tools focusing on retail human resource scheduling. His current research interests are in the theory and computation of predictive control and optimization, and their application to green energy generation, distribution and management.

Official source

Related concepts (27)

Sampling (statistics)

In statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt to collect samples that are representative of the population. Sampling has lower costs and faster data collection compared to recording data from the entire population, and thus, it can provide insights in cases where it is infeasible to measure an entire population.

Nonprobability sampling

Sampling is the use of a subset of the population to represent the whole population or to inform about (social) processes that are meaningful beyond the particular cases, individuals or sites studied. Probability sampling, or random sampling, is a sampling technique in which the probability of getting any particular sample may be calculated. In cases where external validity is not of critical importance to the study's goals or purpose, researchers might prefer to use nonprobability sampling.

Survey sampling

In statistics, survey sampling describes the process of selecting a sample of elements from a target population to conduct a survey. The term "survey" may refer to many different types or techniques of observation. In survey sampling it most often involves a questionnaire used to measure the characteristics and/or attitudes of people. Different ways of contacting members of a sample once they have been selected is the subject of survey data collection.

Simple random sample

In statistics, a simple random sample (or SRS) is a subset of individuals (a sample) chosen from a larger set (a population) in which a subset of individuals are chosen randomly, all with the same probability. It is a process of selecting a sample in a random way. In SRS, each subset of k individuals has the same probability of being chosen for the sample as any other subset of k individuals. A simple random sample is an unbiased sampling technique. Simple random sampling is a basic type of sampling and can be a component of other more complex sampling methods.

Stratified sampling

In statistics, stratified sampling is a method of sampling from a population which can be partitioned into subpopulations. In statistical surveys, when subpopulations within an overall population vary, it could be advantageous to sample each subpopulation (stratum) independently. Stratification is the process of dividing members of the population into homogeneous subgroups before sampling. The strata should define a partition of the population.

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