
Control Systems in Practice, Part 4: Why Time Delay Matters
Time delays exist in two varieties: signal distorting delays, like phase lag, in which each frequency is delayed by a different amount of time, resulting in a distorted signal shape; and non...
See MoreWhat Is Extremum Seeking Control? | Learning-Based Control
Get an introduction to extremum seeking control—an adaptive control method for finding an optimal control input or set of system parameters without needing a model of your system, static...
See MoreHow Kalman Filters Work, Part 1
This article looks at four popular estimation filter architectures: particle filter, sigma point filter, extended Kalman filter, and the Kalman filter. It discusses how all four of these...
See MoreNyquist Stability Criterion, Part 2
An explanation of the Nyquist Stability Criterion part 2. This video steps through the how to sketch a Nyquist plot by hand, what to do if there are open loop pools on the imaginary axis...
See MoreInteractive Tools for Control Purposes
This resource provides different links to Interactive Tools that can be used for control education. Interactive Tools are very powerful educational resources as support to learning and...
See MoreArtificial Intelligence
This lecture discusses artificial intelligence (AI) in the context of data science and machine learning.
See MoreDiscrete control #6: z-plane warping and the bilinear transform
We’re continuing our journey through discrete control and in this video, we’re going to expand our understanding of the bilinear transform. Along the way, we’ll learn about how this...
See MoreBut what is the Fourier Transform? A visual introduction.
An animated introduction to the Fourier Transform.
See MoreUnderstanding Kalman Filters, Part 5: Nonlinear State Estimators
This video explains the basic concepts behind nonlinear state estimators, including extended Kalman filters, unscented Kalman filters, and particle filters.
A Kalman filter is only defined...
See MoreKoopman Spectral Analysis (Control)
In this video, we explore extensions of Koopman theory for control systems. Much of the excitement and promise of Koopman operator theory is centered around the ability to represent...
See MoreWhat Is Fuzzy Logic | Fuzzy Logic Part 1
This video introduces fuzzy logic and explains how you can use it to design a fuzzy inference system (FIS), which is a powerful way to use human experience to design complex systems...
See MoreLinear Regression
Linear regression is a cornerstone of data-driven modeling; here we show how the SVD can be used for linear regression.
See More3-DOF Orientation Tracking with IMUs
This document is not meant to be a comprehensive review of orientation tracking for virtual reality applications but rather an intuitive introduction to inertial measurement units (IMUs) and...
See MoreTrimming and Linearization, Part 2: The Practical Side of Linearization
With a general understanding of linearization, you might run into a few snags when trying to linearize realistic nonlinear models. These snags can be avoided if you have a more practical...
See MoreNyquist Stability Criterion, Part 1
An explanation of the Nyquist Stability Criterion. This video steps through the importance of the criterion, how to interpret the Nyquist plot graphically, and why it is the way it is....
See MorePerspectives on Control-Relevant Identification Through the Use of Interacti...
This paper presents a control-relevant identification methodology through an intuitive interactive tool called "Interactive Tool for Control Relevant Identification (ITCRI)". ITCRI...
See MoreThe Institute for Systems Theory and Automatic Control MATLAB Apps
The Institute for Systems Theory and Automatic Control offers 5 Matlab Apps on the topics of the Nyquist Criterion, Robustness and Stability, Loopshaping, Controllability and Observability...
See MoreData-Driven Dynamical Systems Overview
This video provides a high-level overview of this new series on data-driven dynamical systems. In particular, we explore the various challenges in modern dynamical systems, along with...
See MoreTrimming and Linearization, Part 1: What is Linearization?
Why go through the trouble of linearizing a model? To paraphrase Richard Feynman, it’s because we know how to solve linear systems. With a linear model we can more easily design a controller...
See MoreWhy Choose Deep Learning? Deep Learning for Engineers, Part 1
This video introduces deep learning from the perspective of solving practical engineering problems. The goal is to provide an introduction to the range of practical engineering problems that...
See MoreManuscript about ITISE: an Interactive Software Tool for System Identificati...
The paper describes the conceptual basis, main features and functionality of an interactive software tool developed in support of system identification education and discovery.
This...
See MoreMachine Learning & Text Processing Lectures
This is the video lecture collection by Victor Lavrenko.
See MoreDiscrete control #4: Discretize with the matched method
This is the fourth video on discrete control and in this video we are going to continue exploring the different techniques we can use to discretize a continuous system and talk about the...
See MoreAdvances in feedforward control for measurable disturbances (in Spanish)
The efficient compensation of load disturbances is one of the most important tasks in any control system. Most industrial processes are affected by disturbances and only feedback is commonly...
See MoreSystem Identification: Koopman with Control
This lecture provides an overview of the use of modern Koopman spectral theory for nonlinear control. In particular, we develop control in a coordinate system defined by eigenfunctions of...
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