System Identification Overview

System identification is a methodology for building mathematical models of dynamic systems using measurements of the input and output signals of the system.  This overview from Mathworks...

Introducing Feedback Control to Middle and High School STEM Students, Part 1...

This paper was presented at the 2019 IFAC Advances on Control Education Conference (IFAC-ACE), Philadelphia, PA, USA, July 7-9, 2019, and is in the conference proceedings. This paper aims at...

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...

Kalman Filter Virtual Lab

The Kalman Filter virtual laboratory contains interactive exercises that let you study linear and extended Kalman filter design for state estimation of a simple pendulum system. The virtual...

ITCLI: An Interactive Tool for Closed-Loop Identification

The Interactive Tool for Closed-Loop Identification (ITCLI) is an interactive software tool for understanding SISO closed-loop identification using prediction-error techniques. The tool...

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...

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...

DC Motor Speed: System Modeling

This examples walks through modeling a simple DC motor in MATLAB.

Kalman Filter Design

This example shows how to perform Kalman filtering. Both a steady state filter and a time varying filter are designed and simulated.

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...

Discrete Fourier Transform

The discrete Fourier transform, or DFT, is the primary tool of digital signal processing. The foundation of the product is the fast Fourier transform (FFT), a method for computing the DFT...

Understanding Kalman Filters, Part 4: An Optimal State Estimator Algorithm

Discover the set of equations you need to implement a Kalman filter algorithm. You’ll learn how to perform the prediction and update steps of the Kalman filter algorithm, and you’ll see how...

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...

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...

Introduction to Noise Filtering

Introduction to filtering - moving average, first-order, anti-aliasing, set point softening

What's a Control System and Why Should I Care? A whirlwind tour through the ...

This paper aims to provide some introduction, a cheat sheet, and some context for college level STEM students about to take that first controls class. In some cases, it provides context...

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...

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...

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...

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...

Understanding the Z-Transform

This intuitive introduction shows the mathematics behind the Z-transform and compares it to its similar cousin, the discrete-time Fourier transform. Mathematically, the Z-transform is...

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...

Model 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...

Understanding Control Systems: Feedback Control Systems

This video provides introductory examples to learn about the basics of feedback control (closed-loop control) systems.

Learn how feedback control is used to automate processes and discover...