TikZ source Code: Two systems in negative feedback

TikZ source Code: Two systems in negative feedback

Cayley-Hamilton Theorem [Control Bootcamp]

Here we describe the Cayley-Hamilton Theorem, which states that every square matrix satisfies its own characteristic equation. This is very useful to prove results related to...

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.

Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 5 - Value Fun...

Professor Emma Brunskill

Assistant Professor, Computer Science

Stanford AI for Human Impact Lab

Stanford Artificial Intelligence Lab

Statistical Machine Learning Group

Tuning a Fuzzy Logic Controller with Data | Fuzzy Logic, Part 4

This video covers the basics of data-driven approaches to tuning fuzzy inference systems. See what it means to find an optimal solution, which fuzzy inference parameters are being tuned...

RL Course by David Silver - Lecture 1: Introduction to Reinforcement Learnin...

Introduces reinforcment learning (RL), an overview of agents and some classic RL problems.

Data-Driven Control: Balanced Models with ERA

In this lecture, we connect the eigensystem realization algorithm (ERA) to balanced proper orthogonal decomposition (BPOD). In particular, if enough data is collected, then ERA produces...

TikZ source Code: Sliding Mode Control Example System 2

TikZ source Code: Sliding Mode Control Example System 2

Control Bootcamp: Observability Example in Matlab (Part 2)

This video continues to explore observability in Matlab on the example system of an inverted pendulum on a cart. We look at the observability Gramian.

SVD: Eigen Action Heros [Matlab]

This video describes how the singular value decomposition (SVD) can be used to efficiently represent human faces. In this example, we represent action heros (Matlab).

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...

Data-Driven Control: Observer Kalman Filter Identification

In this lecture, we introduce the observer Kalman filter identification (OKID) algorithm. OKID takes natural input--output data from a system and estimates the impulse response, for later...

Principal Component Analysis (PCA) [Matlab]

This video describes how the singular value decomposition (SVD) can be used for principal component analysis (PCA) in Matlab.

Unitary Transformations and the SVD [Matlab]

This video describes how the singular value decomposition (SVD) is related to unitary transformations, with Matlab code.

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...

Drone Simulation and Control, Part 3: How to Build the Flight Code

This video describes how to create quadcopter flight software from the control architecture developed in the last video. It covers how to process the raw sensor readings and use them with...

Homogeneous Linear Ordinary Differential Equations

In this video we discuss how to solve homogeneous linear ordinary differential equations (ODEs). The approach outlined in this lecture is applicable to high...

Final Value Theorem and Steady State Error

This Final Value Theorem is a way we can determine what value the time domain function approaches at infinity but from the S-domain transfer function. This is very helpful when we're trying...

MATLAB Sensor Array Analyzer App

The Sensor Array Analyzer app enables you to construct and analyze common sensor array configurations. These configurations range from 1-D to 3-D arrays of antennas, sonar transducers, and...

Understanding Sensor Fusion and Tracking, Part 4: Tracking a Single Object W...

This video describes how we can improve tracking a single object by estimating state with an interacting multiple model filter. We will build up some intuition about the IMM filter and show...

Types of Machine Learning 2

This lecture gives an overview of the main categories of machine learning, including supervised, un-supervised, and semi-supervised techniques, depending on the availability of expert labels...

Cartesian, Polar, Cylindrical, and Spherical Coordinates

In this video we discuss Cartesian, Polar, Cylindrical, and Spherical coordinates as well as develop forward and reverse transformations to go from one coord...

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...

Linearizing a Simulink Model Using the Linear Analysis Tool and ‘linmod’

In this video we show how to linearize a non-linear Simulink model using numerical techniques. This approach is extremely powerful as it allows automatic ge...

The Fourier Transform and Convolution Integrals

This video describes how the Fourier Transform maps the convolution integral of two functions to the product of their respective Fourier Transforms.