RADAR Engineering

Radar technology is used widely today. The principles involved are very fundamental and every engineering student studies them at least once. This playlist covers Radar Engineering for an EE...

Advances in Feedforward Control for Measurable Disturbances (slides)

These slides present several contributions to improve the feedforward control approaches when inversion problem arise:  the ideal compensator may not be realizable due to negative delay...

Data-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...

Vibrational Control in Insect Flight

Abstract: It is generally accepted among biology and engineering communities that insects are unstable at hover. However, existing approaches that rely on direct averaging do not fully...

Manuscript 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...

Extremum Seeking Control in Matlab

This lecture explores extremum-seeking control (ESC) on a simple example in Matlab. In particular, a discrete-time (digital) version of ESC is coded in a Matlab script.

Why 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...

Using Simscape™ to Model a Quanser QUBE-Servo 2 with Friction

Modelling a DC servomotor is one of the common examples used in control system textbooks and courses. Given that so many systems use DC motors, e.g. robot manipulator arms, it’s an important...

Virtual Labs for control education

This resource provides different links to virtual and remote labs that can be used for control education. Virtual and remote labs are very powerful tools for learning and teaching, that...

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...

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...

Discrete 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...

Linear Regression

Linear regression is a cornerstone of data-driven modeling; here we show how the SVD can be used for linear regression.

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 Function: ztrans

ztrans(f) finds the Z-Transform of f. By default, the independent variable is n and the transformation variable is z. If f does not contain n, ztrans uses symvar. 

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...

Trimming 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...

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...

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...

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...