What Is System Identification? | System Identification, Part 1

Get an introduction to system identification that covers what it is and where it fits in the bigger picture. See how the combination of data-driven methods and physical intuition can improve...

State Space, Part 1: Introduction to State-Space Equations

Let’s introduce the state-space equations, the model representation of choice for modern control. This video is the first in a series on MIMO control and will provide some intuition around...

Control Systems in Practice, Part 3: What is Feedforward Control?

A control system has two main goals: get the system to track a setpoint, and reject disturbances. Feedback control is pretty powerful for this, but this video shows how feedforward control...

Understanding Kalman Filters, Part 1: Why Use Kalman Filters?

Discover common uses of Kalman filters by walking through some examples. A Kalman filter is an optimal estimation algorithm used to estimate states of a system from indirect and uncertain...

Everything You Need to Know About Control Theory

Control theory is a mathematical framework that gives us the tools to develop autonomous systems. Walk through all the different aspects of control theory that you need to know.

Some of the...

Understanding PID Control, Part 1: What is PID Control?

Chances are you’ve interacted with something that uses a form of this control law, even if you weren’t aware of it. That’s why it is worth learning a bit more about what this control law is...

Understanding PID Control, Part 5: Three Ways to Build a Model

Tuning a PID controller requires that you have a representation of the system you’re trying to control. This could be the physical hardware or a mathematical representation of that hardware...

Control Systems in Practice, Part 2: What is Gain Scheduling?

Often, the best control system is the simplest. When the system you’re trying to control is highly nonlinear, this can lead to very complex controllers. This video continues our discussion...

How a Kalman Filter Works in Pictures

This article builds up some intuition about the Kalman filter using pictures before diving into the mathematics.  A beginner will come away with an understanding of what the Kalman filter is...

The Kalman Filter

This article introduces the Kalman filter at a high level and tries to provide some insight into how the filter is able to estimate state by combining measurements and models. 

This is an...

Robust Control, Part 1: What Is Robust Control?

This videos covers a high-level introduction to robust control. The goal is to get you up to speed with some of the terminology and to give you a better understanding of what robust control...

State Space, Part 4: What is LQR control?

The Linear Quadratic Regulator (LQR)LQR is a type of optimal control that is based on state space representation. In this video, we introduce this topic at a very high level so that you walk...

Understanding Model Predictive Control, Part 1: Why Use MPC?

Learn about the benefits of using model predictive control (MPC). MPC can handle multi-input multi-output (MIMO) systems that have interactions between their inputs and outputs. Due to these...

Reinforcement Learning for Engineers, Part 1: What Is Reinforcement Learning...

Artificial intelligence, machine learning, deep neural networks. These are terms that can spark your imagination of a future where robots are thinking and evolving creatures.In this video...

State Space, Part 3: A Conceptual Approach to Controllability and Observabil...

This video helps you answer two really important questions that come up in control systems engineering: Is your system controllable? And is it observable? Assuming you have a good linear...

Pulse-Doppler Radar | Understanding Radar Principles

his video introduces the concept of pulsed doppler radar. Learn how to determine range and radially velocity using a series of radar pulses. We cover pulse compression and the matched filter...

Linear System Identification | System Identification, Part 2

Learn how to use system identification to fit and validate a linear model to data that has been corrupted by noise and external disturbances Noise and disturbances can make it difficult to...

State Space, Part 2: Pole Placement

This video provides an intuitive understanding of pole placement, also known as full state feedback. This is a control technique that feeds back every state to guarantee closed- loop...

Understanding PID Control, Part 7: Important PID Concepts

Now that you ’ve gotten an overview of PID tuning techniques, this video moves on to discussing two important concepts in PID control: cascaded loops and discrete systems. Both concepts are...

Robust Control, Part 2: Understanding Disk Margin

As we discussed in the last video, mathematical models aren’t a perfect representation of real systems. Therefore, we want to make sure that any system that is designed with those models is...

Robust Control, Part 5: H Infinity and Mu Synthesis

This video walks through a controller design for an active suspension system. Actually, we design two controllers. For the first, we use H infinity synthesis to design a controller for a...

Control Systems in Practice, Part 10: Nichols Chart, Nyquist Plot, and Bode ...

Explore three popular methods to visualize the frequency response of a linear time-invariant (LTI) system: the Nichols chart, the Nyquist plot, and the Bode plot. Learn about each method...

Understanding Sensor Fusion and Tracking, Part 1: What Is Sensor Fusion?

This video provides an overview of what sensor fusion is and how it helps in the design of autonomous systems. It also covers a few scenarios that illustrate the various ways that sensor...

Radar Tutorial (English)

This page provides a detailed overview of radar principles and technologies, including mathematical, physical and technical explanations. “Radartutorial” explains the fundamentals of radar...

Autonomous Navigation, Part 2: Understanding the Particle Filter

This video presents a high-level understanding of the particle filter and shows how it can be used in Monte Carlo localization to determine the pose of a mobile robot inside a building. We...