
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...
See MoreStanford Engineering Everywhere: CS223A - Introduction to Robotics
The purpose of this course is to introduce you to basics of modeling, design, planning, and control of robot systems. In essence, the material treated in this course is a brief survey of...
See MoreFree Video Course in Radar Systems Engineering
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 MoreAn Introduction to the Kalman Filter
The purpose of this paper is to provide a practical introduction to the discrete Kalman filter. This introduction includes a description and some discussion of the basic discrete Kalman...
See MoreReinforcement Learning for Engineers, Part 4: The Walking Robot Problem
This video shows how to use the reinforcement learning workflow to get a bipedal robot to walk. It also looks at how to modify the default example to make it look more like how one would set...
See MoreAn introduction to Beamforming
This video talks about how we actually have more control over the shape of the beam than just adding additional elements, or adjusting the position and orientation of the elements. We can...
See MoreUnderstanding 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...
See MoreAutonomous 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...
See MoreRadar 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...
See MoreSystem 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...
See MoreIntroducing 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...
See MoreKalman 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...
See MoreITCLI: 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...
See MoreDiscrete 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...
See MoreKalman Filter Design
This example shows how to perform Kalman filtering. Both a steady state filter and a time varying filter are designed and simulated.
See MoreAdaptive 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...
See MoreDC Motor Speed: System Modeling
This examples walks through modeling a simple DC motor in MATLAB.
See MoreModel 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...
See MoreControl 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...
See MoreUnderstanding 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...
See MoreUnderstanding 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...
See MoreModeling 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...
See MoreMachine 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...
See MoreWhat'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...
See MoreReinforcement 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...
See More