Feedforward Control Introduction

I introduce feedforward control (FFC) and describe how it can be used to minimize the difference between an output's setpoint and measured value (the error o...

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RL Course by David Silver - Lecture 10: Classic Games

An overview of Game Theory, minimax search, self-play and imperfect information games.

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Control systems with non-minimum phase dynamics

This video describes control systems that have non-minimum phase dynamics, characterized by a zero of the input--output transfer function in the right-half-plane. Physically, these systems...

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Frequency Response Analysis FRA and the Amplitude Ratio and Phase Angle

Process engineers model output response to inputs that oscillate via frequency response analysis (FRA). In this video, I'll go over amplitude ratios and phas...

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Data-Driven Control: Balanced Truncation

In this lecture, we describe the balanced truncation procedure for model reduction, where a handful of the most controllable and observable state directions are kept for the reduced-order...

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Numerically Calculating Partial Derivatives

In this video we discuss how to calculate partial derivatives of a function using numerical techniques. In other words, these partials are calculated withou...

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Introduction to Full State Feedback Control

In this video we introduce the concept of a full state feedback controller. We discuss how to use this system to place the eigenvalues of the closed loop sys...

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Peter Ponders PID - Feed Forward Theory and Calculations

The formulas for feed forwards are derived for motors and hydraulic actuators. See More

Understanding Sensor Fusion and Tracking, Part 5: How to Track Multiple Obje...

This video describes two common problems that arise when tracking multiple objects: data association and track maintenance. We cover a few ways to solve these issues and provide a general...

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Control Bootcamp: Benefits of Feedback on Cruise Control Example (Part 2)

Here we investigate the benefits of feedback for systems with uncertain dynamics and disturbances, as illustrated on a cruise control example. (Part 2)

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The Fourier Transform

This video will discuss the Fourier Transform, which is one of the most important coordinate transformations in all of science and engineering.

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Derivation and Solution of Laplace’s Equation

In this video we show how the heat equation can be simplified to obtain Laplace’s equation. We investigate how to solve Laplace’s equation using separation ...

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Designing a Lag Compensator with Bode Plot

This video walks through a phase lag compensator example using the Bode Plot method.

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Lecture 29: State space representation

This video explains how a system can be modelled in state space form. Also shown is how to obtain transfer function from State space form See More

Frequency domain – tutorial 1: concept of frequency (with Chinese subtitle)

In this video, the following materials are covered:1) intuitive explanation on the frequency concept 2) what is the relation between time and frequency domai...

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Euler (Gimbal Lock) Explained

In this video we explore Euler Rotations, the most common method for orienting objects in 3d. It's by-product "gimbal lock" can cause headaches for animators because the animated motion can...

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Lecture 19: Lead and PD compensator Design using Root Locus

This video discusses how a Proportional Derivative [PD] controller and a Lead compensator can be designed using Root Locus See More

Frequency domain – tutorial 5: Fourier transform

In this video, we learn about Fourier transform which enables us to travel from time to frequency domain when a signal is not periodic. The learning objectiv...

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Why Transfer Functions Matter

Once we know a process's transfer function we can model how it will respond to an variety of inputs very easily, check it out.

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Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 4 - Model Fre...

Professor Emma Brunskill
Assistant Professor, Computer Science
Stanford AI for Human Impact Lab
Stanford Artificial Intelligence Lab
Statistical Machine Learning Group

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Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 16 - Monte Ca...

Professor Emma Brunskill
Assistant Professor, Computer Science
Stanford AI for Human Impact Lab
Stanford Artificial Intelligence Lab
Statistical Machine Learning Group

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Understanding Model Predictive Control, Part 2: What is MPC?

Learn how model predictive control (MPC) works. Using a simple car example, this video provides insight into an MPC controller’s strategy for finding the optimal steering wheel angle to...

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Introduction to System Stability and Control

This video attempts to provide an intuitive understanding of concepts like stability and stability margin. I briefly describe both of these topics with examples and explain how you can...

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Visually Determining Transfer Functions

Process Control classes can get pretty hard to follow when you lose sight of what transfer functions really are. How do you get them in the first place?

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Smart Projectile State Estimation Using Evidence Theory

This journal article provides a very good practical understanding of Dempster-Shafer theory using sensor fusion and state estimation as the backdrop.

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