
Data-Driven Control: Linear System Identification
Overview lecture on linear system identification and model reduction. This lecture discusses how we obtain reduced-order models from data that optimally capture input--output dynamics.
See MoreAndroid and iPhone E-Learning App for Nyquist Stability Criterion
In our Nyquist App, you can analyze the stability of the closed loop by using the Nyquist stability criterion. With the Nyquist stability criterion, you can determine the stability of the...
See MoreMeasuring Angles with FMCW Radar | Understanding Radar Principles
Learn how multiple antennas are used to determine the azimuth and elevation of an object using Frequency Modulated Continuous Wave (FMCW) radar.
By looking at the phase shift between the...
See MoreAutonomous Navigation, Part 3: Understanding SLAM Using Pose Graph Optimizat...
This video provides some intuition around Pose Graph Optimization—a popular framework for solving the simultaneous localization and mapping (SLAM) problem in autonomous navigation.
We’ll...
See MoreA Hybrid Lab Experience: Blending Hands-on Explorations with the Flexibility...
This case study examines how the Earth and Space Science and Engineering (ESSE) department at York University in Toronto offered a meaningful remote laboratory experience to over 180...
See MoreProjectile Motion Practice Problems
In this video, practice along questions on an important topic of Kinematics i.e Projectile Motion. Practicing would help you remember the concepts and also understand them better.
See MoreIntroduction to Anomaly Detection for Engineers
Anomaly detection is the process of identifying events or patterns that differ from expected behavior. This is important for applications like predictive maintenance but can be hard to...
See MoreMachine Learning: What is easy, medium, and hard?
This video gives a brief overview of what is easy, medium, and hard in machine learning, explored through case studies. Progress in machine learning is rapidly advancing, and changing the...
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 MoreKalman and Bayesian Filters in Python
Introductory text for Kalman and Bayesian filters. All code is written in Python, and the book itself is written using Juptyer Notebook so that you can run and modify the code in your...
See MoreImprove SNR and Capacity of Wireless Communication Using Antenna Arrays
The goal of a wireless communication system is to serve as many users with the highest possible data rate given constraints such as radiation power limit and operating budget. To improve the...
See MorePID Controller Variations
It is important to understand the variations on the PID algorithm when tuning and when choosing a version that is consistent within your use context. Unfortunately, there are many names for...
See MoreThe Linear Quadratic Regulator
In these notes, we will derive the solution to the finite-horizon linear quadratic regulator (LQR) problem in several different ways. Fundamentally, LQR can be viewed as a large least...
See MoreMATLAB Example: Doppler Estimation
This example shows a monostatic pulse radar detecting the radial velocity of moving targets at specific ranges. The speed is derived from the Doppler shift caused by the moving targets. We...
See MoreBumpless Transfer and Tuning
Switching from MAN to AUTO mode or LOCAL to CASCADE or changing the controller integral time should not cause a change in the controller output, a bump. But a primitive coding of the PID...
See MoreRobotic Car - A Simple Way to Build a Model
You don't always have to work out the math in order to build up a model of your system. Sometimes generating a model is as easy as running a simple test and inspecting the results. I show...
See MoreControl Bootcamp: Introduction to Robust Control
This video motivates robust control with the famous 1978 paper by John Doyle, titled "Guaranteed Margins for LQG Regulators"... Abstract: There are none.
See MoreQuanser QUBE-Servo 2: Low-cost Teaching Platform for Controls
The Quanser QUBE-Servo 2 is a fully integrated, modular servomotor lab experiment designed for teaching mechatronics and control concepts at the undergraduate level. Ideal for teaching speed...
See MoreMATLAB Command: goodnessOfFit
Goodness of fit between test and reference data for analysis and validation of identified models
See MoreSystem Identification: Regression Models
This lecture provides an overview of modern data-driven regression methods for linear and nonlinear system identification, based on the dynamic mode decomposition (DMD), Koopman theory, and...
See MoreDSP Lecture 1: Signals
This is a video lecture for ECSE-4530 Digital Signal Processing by Rich Radke, Rensselaer Polytechnic Institute.
See MoreOnline Fault Detection for a DC Motor
Program embedded processors to estimate parameters and detect changes in motor dynamics in real time using System Identification Toolbox™.
See MoreWhat Is a Control System and Why Should I Care?
This is a 25 minute abbreviated version of the Part 1 & Part 2 talk. It goes through the basic ideas while skipping some of the details and examples of the longer talks. The talk abstract...
See MoreControl Bootcamp: LQG Example in Matlab
This video combines the LQR and Kalman filter in Matlab on the example of an inverted pendulum on a cart. We stabilize the full nonlinear system with a measurement of a single variable (the...
See MoreSystems Engineering, Part 5: Some Benefits of Model-Based Systems Engineerin...
Learn how model-based systems engineering (MBSE) can help you cut through the chaos of early systems development and get you from definition to execution more seamlessly.
You’ll hear the...
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