
EGGN 510 - Lecture 02-1 Digital Image Fundamentals
This is a video lecture of EGGN 510 Image and Multidimensional Signal Processing by William Hoff.
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 MoreWhat's a Control System and Why Should I Care?
This paper is designed as a primer for college level STEM students about to take their first formal class in feedback control systems. This means that the explanations assume the reader has...
See MoreVarious games for learning Controller Design
Since 2005, we are using educational games in the course „Einführung in die Regelungstechnik“ (Introduction to automatic control).
The project started with the game spaceballRT, which uses...
See MoreModel Reference Adaptive Control of Aircraft Undergoing Wing Rock
This example shows how to control roll and roll rate of a delta wing aircraft undergoing wing rock. For this example, the system model is unknown. Therefore, you use model reference adaptive...
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 MoreVirtual 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...
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 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 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 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 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 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 MoreThe Demod Squad: A Tutorial on the Utility and Methodologies for Using Modul...
This paper is a brief tutorial on methods for using modulated signals in feedback loops, and especially of the different methods and trade offs used for demodulating those signals to get...
See MoreThe Kalman Filter [Control Bootcamp]
Here, we discuss the Kalman Filter, which is an optimal full-state estimator, given Gaussian white noise disturbances and measurement noise.
See MoreSending digital information over a wire | Networking tutorial (1 of 13)
This video lecture is the beginning of an exploration of computer networking with the basics of sending digital information with a copper wire.
Why is a Chirp Signal used in Radar?
Gives an intuitive explanation of why the Chirp signal is a good compromise between an impulse waveform and a sinusoidal pulse waveform for radar.
See MoreMathworks Model Reference Adaptive Control Overview
This website provides an overview of the mathematics behind Model Reference Adaptive Control (MRAC). MRAC is a model-based, real-time adaptive control algorithm that computes control actions...
See MoreWhat Is Online Estimation?
This Mathworks document describes online estimation. Online estimation algorithms estimate the parameters and states of a model when new data is available during the operation of the...
See MoreRobotic Car - How to read Gyro Datasheets (Part 1)
Have you ever been lost trying to understand the information in a gyro datasheet? This video should help! In this first part I go through the mechanical characteristics of a MEMS gyro and...
See More1D Kinematics - Speed, Velocity, Acceleration
Walter Lewin is one of the most reputed professors and was a former lecturer at MIT. His free to watch series on YouTube titled 8.01 is an excellent one for undergrads and high school...
See MoreRadar Systems Engineering Lecture 4: The Radar Equation
This Free Radar Systems Engineering Course (video, audio and screen captured ppt slides) and separate pdf slides) has been developed as a first course in Radar Systems for first year...
See More