Relationship Between Poles and Performance of a Dynamic System

In this video we establish the relationship between pole locations and associated performance of a dynamic system. This relationship is useful to translate ...

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Controllability [Control Bootcamp]

This lecture explores when a linear system is controllable. We begin with the simple test in terms of the rank of the controllability matrix on a few intuitive examples.

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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...

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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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Data-Driven Control: BPOD and Output Projection

In this lecture, we introduce the output projection for balancing proper orthogonal decomposition (BPOD), to reduce the number of adjoint simulations required when the number of measurements...

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Data-Driven Control: ERA and the Discrete-Time Impulse Response

In this lecture, we describe how the discrete-time impulse response is used in the eigensystem realization algorithm (ERA).

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Internal Model Control Example Problem

I walk through how to design a feedback controller based on a given process transfer function, using Internal Model Control.

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Peter Ponders PID - Controlling an Under Damp Mass and Spring System

Demonstrates:How to calculate the PID gains. The importance of the derivative gain. How to simulate the mass and spring systemControl limitations based on s...

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Drone Simulation and Control, Part 2: How Do You Get a Drone to Hover?

In the last video, we showed we can manipulate the four motors of a quadcopter to maneuver it in 3D space by getting it to roll, pitch, yaw, and change its thrust. We also covered the four...

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Time domain - tutorial 3: signal transformations

In this video, we learn how different transformations can change the signal shape. Specifically, we cover time shifting & scaling as well as amplitude shift...

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Direct Synthesis Method Numerator Dynamics Problem

I walk through how to design a PID feedback controller when given a second order process with numerator dynamics, using the Direct Synthesis Method.

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Peter Ponders PID - Why PID with 2nd Derivative Gain?

If you have ever tuned a hydraulic system and wondered why PID control didn't work better than PI control the answer is here. Since the 1980s people have kn...

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Fourier Analysis: Overview

This video presents an overview of the Fourier Transform, which is one of the most important transformations in all of mathematical physics and engineering. This series will introduce the...

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Neural Network Architectures

This lecture describes the wide variety of neural network architectures available to solve various problems.

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Frequency domain – tutorial 7: Fourier transform examples marathon

In this video, we solve lots of lots examples to practice how to quickly find Fourier transform using table of pairs and properties. The learning objective i...

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Predicting Second Order Transfer Function Behavior

Given a second order transfer function, I'll cover how we can predict the system behavior and derive the appropriate time constants and damping coefficient.

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Lecture 4: Electromechanical system Transfer functions and Analogous circuit...

The transfer function of a dc motor driving a mechanical load [an electromechanical system] has been derived following which a quarter car model is discussed... See More

Understanding PID Control, Part 3: Expanding Beyond a Simple Derivative

This video describes how to make an ideal PID controller more robust when controlling real systems that don’t behave like ideal linear models. Noise is generated by sensors and is present in...

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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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Standard 2nd Order ODEs: Natural Frequency and Damping Ratio

In this video we discuss writing 2nd order ODEs in standard form xdd(t)+2*zeta*wn*xd(t)+wn^2*x(t)where zeta = damping ratio wn = natural ...

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RL Course by David Silver - Lecture 5: Model Free Control

Dives into On Policy Monte-Carlo Control and Temporal Difference Learning, as well as Off-Policy Learning.

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Numerically Solving Partial Differential Equations

In this video we show how to numerically solve partial differential equations by numerically approximating partial derivatives using the finite difference me...

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Posicast Control 5 - (In English)

In this video Posicast in closed-loop is illustrated using a gantry-crane system simulations.

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Control System with MATLAB - Block Diagram Reduction

Carrying out block reduction using MATLAB has been explained in this video See More

Understanding The Sensitivity Function

In this video I explain the sensitivity function and try to demystify the equation used to solve for the nominal sensitivity peak. Sensitivity describes how much process variations affect...

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