Control Systems in Practice, Part 8: The Gang of Six in Control Theory

When analyzing feedback systems, we can get caught up thinking solely about the relationship between the reference signal and the output. However, to fully understand how a feedback system...

EGGN 510 - Lecture 02-1 Digital Image Fundamentals

This is a video lecture of EGGN 510 Image and Multidimensional Signal Processing by William Hoff.

Using the Reinforcement Learning Toolbox™ to Balance an Inverted Pendulum

Reinforcement learning (RL) is a subset of Machine Learning that uses dynamic data, not static data like unsupervised learning or supervised learning. Reinforcement learning is used in many...

Discrete 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...

Nyquist 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...

ITCRI: An Interactive Software Tool for Control-Relevant Identification

The Interactive Tool for Control Relevant Identification (ITCRI) comprehensively captures the control-relevant identification process, from input design to closed-loop control, depicting...

MATLAB Function: phased.MVDRBeamformer

The phased.MVDRBeamformer System object™ implements a narrowband minimum-variance distortionless-response (MVDR) beamformer. The MVDR beamformer is also called the Capon beamformer. An MVDR...

What 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...

MATLAB Example: Fault Detection Using an Extended Kalman Filter

This example shows how to use an extended Kalman filter for fault detection. The example uses an extended Kalman filter for online estimation of the friction of a simple DC motor...

Why Choose Model-Based Reinforcement Learning?

What is the difference between model-free and model-based reinforcement learning? Explore the differences and results as the learning models are applied to balancing a cart/pole system as an...

MIMO Radar: TI Application Report

MIMO radar is a key technology in improving the angle resolution (spatial resolution) of mmwave-radars. This article introduces the basic principles of the MIMO-radar and the different...

Understanding Control Systems: The Disturbance Rejection Problem

This video provides a demonstration using a car to show how you can simulate open- and closed-loop systems in Simulink®.

First, you will learn how to model and tune open-loop systems. The...

Artificial Intelligence

This lecture discusses artificial intelligence (AI) in the context of data science and machine learning.

Algorithms for Automated Driving

Each chapter of this (mini-)book guides you in programming one important software component for automated driving. Currently, this book contains two chapters: Lane Detection, and Control...

3-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...

Discrete control #2: Discretize! Going from continuous to discrete domain

This is the second video in the discrete control series. It focuses on discretizing a continuous system - getting to the z-domain from the s-domain.

MATLAB Example: Train MBPO Agent to Balance Cart-Pole System

This example shows how to train a model-based policy optimization (MBPO) agent to balance a cart-pole system modeled in MATLAB. For more information on MBPO agents, see Model-Based Policy...

Control Bootcamp: Kalman Filter Example in Matlab

This lecture explores the Kalman Filter in Matlab on an inverted pendulum on a cart.

Interactive 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...

Trimming 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...

Thoughts on Furthering the Control Education of Practicing Engineers

This paper was presented at the 2019 IFAC Advances on Control Education Conference (IFAC-ACE), Philadelphia, PA, USA, July 7-9, 2019, and is in the conference proceedings. This paper aims to...

Nyquist 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....

ITSIE: An Interactive Software Tool for System Identification Education

ITSIE is an Interactive Tool for System Identification Education. The tool is developed using Sysquake, a Matlab-like language with fast execution and excellent facilities for interactive...

Conventional and Adaptive Beamformers

This example illustrates how to apply digital beamforming to a narrowband signal received by an antenna array. Three beamforming algorithms are illustrated: the phase shift beamformer...

What 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...