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

Control Systems Lectures - Transfer Functions

This lecture describes transfer functions and how they are used to simplify modeling of dynamic systems.

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SVD: Optimal Truncation [Matlab]

This video describes how to optimally truncate the singular value decomposition (SVD) for noisy data (Matlab code).

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Understanding and Sketching Individual Bode Plot Components

In this video we illustrate how 7 types of simple transfer functions contribute to a bode plot. We refer to these as ‘components’ and will cover the followi...

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TikZ source Code: Multiplication of system variables

TikZ source Code: Multiplication of system variables

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Digital Twin Parameter Tuning

Learn how to tune the digital twin model of a pump system to its physical asset using Simulink Design Optimization™. You can use measured data collected from the physical system to tune the...

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Controllability and the Discrete-Time Impulse Response [Control Bootcamp]

This lecture derives the impulse response for a discrete-time system and relates this to the controllability matrix.

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Control Bootcamp: Three Equivalent Representations of Linear Systems

This video explores three equivalent representations of linear systems: State-space ODEs, Frequency domain transfer functions, and Time-domain impulse response convolution.

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Derivation of the 2D Wave Equation

In this video we derive the 2D wave equation. This partial differential equation governs the motion of waves in a plane and is applicable for thin vibrating...

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TikZ source Code: Sliding Mode Control Example System 1

TikZ source Code: Sliding Mode Control Example System 1

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Computing Euler Angles: The Euler Kinematical Equations and Poisson’s Kinema...

In this video we discuss how the time rate of change of the Euler angles are related to the angular velocity vector of the vehicle. This allows us to design an algorithm to consume...

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Data-Driven Control: Balancing Example

In this lecture, we give an example of how a change of coordinates can balance the controllability and observability of an input—output system.

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Machine Learning Goals

This lecture discusses the high-level goals of machine learning, and what we want out of our models. Goals include speed and accuracy, along with interpretability, generalizability...

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Frequency domain – tutorial 8: frequency spectra

In this video, we learn about frequency spectra which can be divided into two parts: phase and magnitude spectrum. Some examples will be provided to practice...

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What Is a Control System and Why Should I Care? (Part 2)

This talk gives a glimpse of some of the methods and math that allow us to understand feedback systems. Continuing on from Part 1, it gives a description of how we use scientific principles...

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Discrete control #1: Introduction and overview

So far I have only addressed designing control systems using the frequency domain, and only with continuous systems. That is, we’ve been working in the S domain with transfer functions. We...

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Robust Principal Component Analysis (RPCA)

Robust statistics is essential for handling data with corruption or missing entries. This robust variant of principal component analysis (PCA) is now a workhorse algorithm in several fields...

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Frequency domain – tutorial 11: equalization

In this video, we learn about equalization technique which is used in communication systems to compensate for the destructive effect of the channel between t...

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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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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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Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 9 - Policy Gr...

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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Inputs and Outputs as defined by a Process Control Engineer

Defining process inputs and outputs is a lot more complicated than I initially thought when I was learning about process control. In this video, I share how ...

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NASA's General Mission Analysis Tool (GMAT)

NASA's GMAT is the worlds only enterprise, multi-mission, open source software system for space mission design, optimization, and navigation. The system supports missions in flight regimes...

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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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Fourier Series: Part 1

This video will show how to approximate a function with a Fourier series, which is an infinite sum of sines and cosines. We will discuss how these sines and cosines form a basis for the...

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