Understanding Sensor Fusion and Tracking, Part 2: Fusing a Mag, Accel, and G...

This video describes how we can use a magnetometer, accelerometer, and a gyro to estimate an object’s orientation. The goal is to show how these sensors contribute to the solution, and to...

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Time Domain Analysis: Performance Metrics for a First Order System

In this video we introduce the concept of time domain analysis for dynamic systems. We examine a first order dynamic system and derive how various performan...

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PID Control with Posicast 7 - ( In English )

In this video closed-loop configurations with PID controllers and Posicast are introduced.

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Lecture 30: Canonical Forms

In this video, phase variable canonical form or Controllable Canonical form as well as Observable Canonical form of state space representation is explained See More

Koopman Spectral Analysis (Representations)

In this video, we explore how to obtain finite-dimensional representations of the Koopman operator from data, using regression.

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Solving the 1D Heat Equation

In this video we simplify the general heat equation to look at only a single spatial variable, thereby obtaining the 1D heat equation. We solving the result...

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Bode Plots by Hand: Poles and Zeros at the Origin

This is a continuation of the Control Systems Lectures. This video describes the benefit of being able to approximate a Bode plot by hand and explains what a Bode plot looks like for a...

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SVD and Optimal Truncation

This video describes how to truncate the singular value decomposition (SVD) for matrix approximation.

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Laplace domain – tutorial 4: Laplace transform examples

In this video, we solve lots of examples to practice how to quickly find Laplace transform using the table of pairs & properties and five golden rules on ROC...

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Stanford CS229: Machine Learning | Autumn 2018

Autumn 2018 Stanford course on machine learning by Andrew Ng.

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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...

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Process Control Introduction

An overview on state variables, inputs (manipulated and disturbance variables), outputs (measured state variables), and an example on the balance equations w...

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Frequency domain – tutorial 4: Gibbs phenomenon

In this video, we quickly review the Gibbs phenomenon which involves two facts:1) Fourier sums overshoot at a jump discontinuity2) overshoot does not disapp...

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Digital Twins

This lecture discusses the use of data-driven digital twins in advanced model-based design and engineering, and the related digital thread, which ties together the data throughout an entire...

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IMC based PID Design for a First Order Process

IMC based PID Design for a First Order Process

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Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 15 - Batch Re...

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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Peter Ponders PID - Cascade Control Part2

The inner loop pole locations and gains are calculated first so the inner loop pole locations are determined by the user. The outer loop poles are still pla...

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Extremum Seeking Control: Challenging Example

This lecture explores the use of extremum-seeking control (ESC) to solve a challenging control problem with a right-half plane zero.

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Working with Synthetic Data | Deep Learning for Engineers, Part 2

This video covers the first step in deep learning: having access to data. Part of making the decision of whether deep learning is right for your project comes down to the type and amount of...

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Automatic Updates of Transition Potential Matrices in Dempster-Shafer Networ...

Journal article that develops an evidential reasoning network capable of learning/updating the relationships between Frames of Discernment (the sets over which Dempster-Shafer reasons that...

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SOPDT Sliding Mode Control ( SMC ) with Smith Predictor

I made this simulation SMC with Smith predictor just to see how it would work. I am not a process control guy but so many people struggle with temperature c... See More

Solving the Heat Equation with the Fourier Transform

This video describes how the Fourier Transform can be used to solve the heat equation. In fact, the Fourier transform is a change of coordinates into the eigenvector coordinates for the...

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Principal Component Analysis (PCA)

Principal component analysis (PCA) is a workhorse algorithm in statistics, where dominant correlation patterns are extracted from high-dimensional data.

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Drawing the root locus (Interactive Tool)

This page was developed to help student learn how to sketch the root locus by hand. You can enter a numerator and denominator for G(s)H(s) (i.e., the loop gain) and the program will guide...

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Lecture 12: Steady state error

In this video, steady state error has been explained See More