Data-Driven Control: Overview

Overview lecture for series on data-driven control. In this lecture, we discuss how machine learning optimization can be used to discover models and effective controllers directly from data...

Using Transfer Learning | Deep Learning for Engineers, Part 4

This video introduces the idea of transfer learning. Transfer learning is modifying an existing deep network architecture and then retraining it to accomplish your task rather than the task...

An Artificial Intelligence Primer

This blog post is a great primer providing definitions for basic terms used in AI and machine learning (ML) such as supervised learning, unsupervised learning, and transfer learning...

Multi-Agent Reinforcement Learning: Independent vs Cooperative Agents

From the Abstract:

Intelligent human agents exist in a cooperative social environment that facilitates learning. They learn not only by trialand -error, but also through cooperation by...

Understanding Control Systems (Playlist)

Learn the basic concepts behind controls systems. Walk through everyday examples that outline fundamental ideas, and explore open-loop and feedback control systems. These videos explore open...

Basic course of control theory (Hungarian and English)

The basic course of control theory is taught in the 3rd year for the students specialized in information technology at the Faculty of Electrical Engineering and Informatics of the Budapest...

Controlling Robotic Swarms

Come with me to the Robotics, Aerospace, and Information Networks lab at the University of Washington to learn the basics of swarm robotics. Find out how simple distributed algorithms can...

CS224n: Natural Language Processing with Deep Learning | Winter 2021

This course covers the foundations of the effective modern methods for deep learning applied to NLP, a big picture understanding of human languages and the difficulties in understanding and...

Math Background for Machine Learning from Carnegie Melon University

This course provides a place for students to practice the necessary mathematical background for further study in machine learning — particularly for taking 10-601 and 10-701. Topics covered...

Control Systems in Practice, Part 1: What Control Systems Engineers Do

This video walks through the phases of a typical project and describes what it means to be a control systems engineer. It covers the concept formulation phase, in which your job is to help...

Systems 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...

Averaging Methods in Nonlinear Dynamical Systems

Perturbation theory and in particular normal form theory has shown strong growth during the last decades. So it is not surprising that the authors have presented an extensive revision of the...

Machine Learning Control: Overview

This lecture provides an overview of how to use machine learning optimization directly to design control laws, without the need for a model of the dynamics.

What is a Matched Filter?

This video explains the Matched Filter from a signals perspective.

Reinforcement Learning: An Introduction

From the book introduction:

The idea that we learn by interacting with our environment is probably the first to occur to us when we think about the nature of learning. When an infant plays...

Advanced process control (APC): Theory & Applications in SAGD

This webinar is presented by Thiago Avila and covers what APC is, why we do it, examples of APC in the SAGD industry, what optimization opportunities are available, and where this technology...

Why multichannel beamforming is useful for wireless communication

Wireless communication systems like 5G and WiFi usually have to serve many users simultaneously and they have to deal with multiple paths between two radios when operating in a scattering...

System Identification: Dynamic Mode Decomposition with Control

This lecture provides an overview of dynamic mode decomposition with control (DMDc) for full-state system identification. DMDc is a least-squares regression technique based on the singular...

The 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...

Machine 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...

Process Dynamics and Control Course

This course focuses on a complete start to finish process of physics-based modeling, data driven methods, and controller design. Although some knowledge of computer programming is required...

Why Padé Approximations Are Great! | Control Systems in Practice

Watch an introduction to Padé approximations. Learn what Padé approximations are and how to calculate them, why they are important, and when to use them—specifically in the context of time...

Control 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.

Model Reference Adaptive Control Fundamentals (Dr. Tansel Yucelen)

Forum on Robotics & Control Engineering (FoRCE, http://force.eng.usf.edu/) Seminar Series: "Model Reference Adaptive Control Fundamentals" (Dr. Tansel Yucelen)

Wind Tunnel Testing: Introduction and Data Acquisition

This is the first of our 3 part series on wind tunnel testing. In this video, we introduce the concept of wind tunnel testing as well as discuss the process for acquiring aerodynamic data in...