Modeling Physical Systems, An Overview

This video sets the stage for the topics that I want to cover over the next month or two. This is an overview of how you go from a physical system to a linear model where you can design a...

Understanding PID Control, Part 6: Manual and Automatic Tuning Methods

The previous video showed three different approaches to developing a mathematical model of your physical system. Now that we have this model, we can use it to tune a PID controller that will...

Machine Learning Control: Tuning a PID Controller with Genetic Algorithms (P...

This lecture shows how to use genetic algorithms to tune the parameters of a PID controller. Tuning a PID controller with genetic algorithms is not generally recommended, but is used to...

Understanding Kalman Filters, Part 3: An Optimal State Estimator

Watch this video for an explanation of how Kalman filters work. Kalman filters combine two sources of information, the predicted states and noisy measurements, to produce optimal, unbiased...

Reinforcement Learning for Engineers, Part 3: Policies and Learning Algorith...

This video provides an introduction to the algorithms that reside within the agent. We’ll cover why we use neural networks to represent functions and why you may have to set up two neural...

The Radar Equation | Understanding Radar Principles

Learn how the radar equation combines several of the main parameters of a radar system in a way that gives you a general understanding of how the system will perform. The radar equation is a...

Z-Transform - Practical Applications

Covering practical applications of the Z-transform used in digital signal processing, for example, stability analysis and frequency response of discrete-time systems. Theory, C code, and...

System Identification Methods

System Identification is the process of determining the model or the equations of motion for your system. This is incredibly important because basing a control system design off of a bad...

Understanding Kalman Filters, Part 2: State Observers

Learn the working principles of state observers, and discover the math behind them. State observers are used for estimating the internal states of a system when you can’t directly measure...

Reinforcement Learning for Engineers, Part 2: Understanding the Environment ...

In this video, we build on our basic understanding of reinforcement learning by exploring the workflow. We cover what an environment is and some of the benefits of training within a...

FMCW Radar for Autonomous Vehicles | Understanding Radar Principles

Watch an introduction to Frequency Modulated Continuous Wave (FMCW) radar and why it’s a good solution for autonomous vehicle applications. This demonstration will show how FMCW radar can...

Understanding PID Control, Part 4: A PID Tuning Guide

It can be difficult to navigate all the resources that promise to explain the secrets of PID tuning. Some proclaim that PID tuning is an art that requires finesse and experience, while...

Advances in feedforward control for measurable disturbances

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...

Using the Control System Designer in Matlab

In this video we show how to use the Control System Designer to quickly and effectively design control systems for a linear system. We show how to add multi...

Control Systems in Practice, Part 9: The Step Response

This video covers a few interesting things about the step response. We’ll look at what a step response is and some of the ways it can be used to specify design requirements for closed loop...

An Introduction to Multi-Agent Reinforcement Learning

Learn what multi-agent reinforcement learning is and some of the challenges it faces and overcomes. You will also learn what an agent is and how multi-agent systems can be both cooperative...

The AVA Flight Computer

This video describes the board design, hardware architecture, and software components of the All Vehicle Avionics (AVA) flight computer that was designed by Joe Barnard of BPS Space.  This...

Adaptive Control Basics: What Is Model Reference Adaptive Control?

Use an adaptive control method called model reference adaptive control (MRAC). This controller can adapt in real time to variations and uncertainty in the system that is being controlled...

Control Systems in Practice, Part 7: 4 Ways to Implement a Transfer Function...

In some situations, it is easier to design a controller or a filter using continuous, s-domain transfer functions. We have a lot of mathematical tools that make analyzing and manipulating...

Autonomous Navigation, Part 4: Path Planning with A* and RRT

This video explores some of the ways that we can use a map like a binary occupancy grid for motion and path planning. We briefly cover what motion planning means and how we can use a graph...

Model Predictive Control

This lecture provides an overview of model predictive control (MPC), which is one of the most powerful and general control frameworks. MPC is used extensively in industrial control settings...

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...

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...

Intro to Data Science: The Nature of Data

This lecture discusses the types of data you might encounter, and how it determines which techniques to use.

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...