
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...
See MoreControl 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...
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 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 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 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 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 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 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 MoreAn 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...
See MoreWhat Are Dynamic Models? Chapter 1 from Dynamic Models in Biology
Throughout this book we use a wide-ranging set of case studies to illustrate different aspects of models and modeling. In this introductory chapter we describe and give examples of different...
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 MoreIntroduction: PID Controller Design
In this tutorial we will introduce a simple, yet versatile, feedback compensator structure: the Proportional-Integral-Derivative (PID) controller. The PID controller is widely employed...
See MoreAutonomous 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...
See MoreThe 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...
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 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 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 MoreUsing 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...
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 MoreReinforcement 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...
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 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 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...
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