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904 items
Control Bootcamp: Full-State Estimation
11 min
Beginner
Video
Theory
This video describes full-state estimation. An estimator dynamical system is constructed, and it is shown that the estimate converges to the true state. Further, the eigenvalues of the...
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Partial Fraction Expansion/Decomposition
59 min
Beginner
Video
Theory
In this video we discuss how to perform partial fraction expansion (PFE) to rewrite a ratio of polynomials as simpler expressions. Topics and time stamps:(0...
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![Controllability [Control Bootcamp]](/sites/default/files/styles/search_resulkts/public/2020-12/maxresdefault_301.jpg?itok=H78kwaJN)
Controllability [Control Bootcamp]
32 min
Beginner
Video
Theory
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.
See More![Linear Regression 3 [Python]](/sites/default/files/styles/search_resulkts/public/2020-12/maxresdefault_438.jpg?itok=rU8HcFY_ "Linear Regression 3 [Python]")
Linear Regression 3 [Python]
10 min
Intermediate
Video
Application
This video describes how the singular value decomposition (SVD) can be used for linear regression in Python (part 3).
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Bode Plots of Complex Transfer Functions
65 min
Beginner
Video
Theory
In this video we discuss how to generate a bode plot of a complex transfer function by decomposing it into the individual components. We then show how one c...
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Time Domain Analysis: Performance Metrics for a First Order System
78 min
Beginner
Video
Theory
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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Virtual Laboratory for Vibrations and Control Theory
Intermediate
Virtual Lab
Application
This virtual lab developed in Matlab Simscape provides an innovative tool for learning and teaching fundamentals of mechanical vibrations including mass-spring-damper systems.
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Data-Driven Control: Change of Variables in Control Systems (Correction)
2 min
Intermediate
Video
Theory
This video corrects a typo in the previous lecture.
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Types of Machine Learning 2
6 min
Beginner
Video
Theory
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...
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Solving the 1D Wave Equation
118 min
Beginner
Video
Theory
In this video, we solve the 1D wave equation. We utilize the separation of variables method to solve this 2nd order, linear, homogeneous, partial differenti...
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Discrete control #5: The bilinear transform
15 min
Beginner
Video
Theory
This is video number five on discrete control and here, we’re going to cover the famous and useful bilinear transform. The bilinear transform is yet another method for converting, or mapping...
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Control systems with non-minimum phase dynamics
8 min
Beginner
Video
Theory
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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Time domain - tutorial 11: system properties from impulse response
13 min
Beginner
Video
Theory
In this video, we learn how to find system properties from the impulse response. Specifically, memoryless, causal, stable and invertible systems will be ful...
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Extremum Seeking Control in Simulink
26 min
Beginner
Video
Application
This lecture explores extremum-seeking control (ESC) on a simple example in Matlab’s Simulink.
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Time domain - tutorial 2: signal representation
2 min
Beginner
Video
Theory
In this video, we review how to represent information as a signal. The information can be anything such as voice (1D) or an image (2D) or even a video (3D). ...
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MIT 6.S191: Introduction to Deep Learning
Beginner
Video
Theory
MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep...
See More![Fourier Series and Gibbs Phenomena [Python]](/sites/default/files/styles/search_resulkts/public/2020-12/maxresdefault_364.jpg?itok=-v1tvO0q "Fourier Series and Gibbs Phenomena [Python]")
Fourier Series and Gibbs Phenomena [Python]
8 min
Beginner
Video
Application
This video will describe how to compute the Fourier Series in Python and Gibbs Phenomena that appear for discontinuous functions.
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Cascade Control Intro
8 min
Beginner
Video
Theory
How can we improve the disturbance rejection of our controllers using additional, relevant measurements? Tune in to find out!
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Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 13 - Fast Rei...
79 min
Intermediate
Video
Theory
Professor Emma Brunskill
Assistant Professor, Computer Science
Stanford AI for Human Impact Lab
Stanford Artificial Intelligence Lab
Statistical Machine Learning Group
Dynamic Modeling in Process Control
14 min
Beginner
Video
Theory
I'll show you how we can build the dynamic models necessary to derive process transfer functions as an introduction to process control.
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RL Course by David Silver - Lecture 9: Exploration and Exploitation
99 min
Intermediate
Video
Theory
An overview of multi-armed bandits, contextual bandits and Markov Decision Processes.
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Peter Ponders PID-Fuzzy Logic vs PID
10 min
Beginner
Video
Theory
There are many academic and engineering papers showing how good fuzzy logic control is relative to PID control. Every FL vs PID paper I have seen compares...
See More![Cayley-Hamilton Theorem [Control Bootcamp]](/sites/default/files/styles/search_resulkts/public/2020-12/maxresdefault_395.jpg?itok=Xi08rPqq "Cayley-Hamilton Theorem [Control Bootcamp]")
Cayley-Hamilton Theorem [Control Bootcamp]
6 min
Beginner
Video
Theory
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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Deploying Deep Learning Models | Deep Learning for Engineers, Part 5
15 min
Beginner
Video
Theory
This video covers the additional work and considerations you need to think about once you have a deep neural network that can classify your data. We need to consider that the trained network...
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