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...

Data-Driven Control: The Goal of Balanced Model Reduction

In this lecture, we discuss the overarching goal of balanced model reduction: Identifying key states that are most jointly controllable and observable, to capture the most input—output...

How the Flight Controller Code Works - dRehmFlight VTOL

This video will walk you through the flight controller code of dRehmFlight VTOL to give you a better understanding of the contents and structure. The hope is that it will cover almost...

Inner Products in Hilbert Space

This video will show how the inner product of functions in Hilbert space is related to the standard inner product of vectors of data.

SVD: Optimal Truncation [Matlab]

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

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

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.

Ratio Control and Scaled Signal Calculations

When and how to use ratio, and how to implement within standard scaled signals

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.

Dynamic Mode Decomposition (Code)

In this video, we code up the dynamic mode decomposition (DMD) in Matlab and use it to analyze the fluid flow past a circular cylinder at low Reynolds number.

Singular Value Decomposition (SVD): Matrix Approximation

This video describes how the singular value decomposition (SVD) can be used for matrix approximation.

Linear Regression 1 [Matlab]

This video describes how the singular value decomposition (SVD) can be used for linear regression in Matlab (part 1).

Principal Component Analysis (PCA) [Matlab]

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

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

RL Course by David Silver - Lecture 10: Classic Games

An overview of Game Theory, minimax search, self-play and imperfect information games.

Data-Driven Control: Eigensystem Realization Algorithm

In this lecture, we introduce the eigensystem realization algorithm (ERA), which is a purely data-driven algorithm to obtain balanced input—output models from impulse response data. ERA was...

Euler Angles and the Euler Rotation Sequence

In this video we discuss how Euler angles are used to define the relative orientation of one coordinate frame to another.

Parseval's Theorem

Parseval's theorem is an important result in Fourier analysis that can be used to put guarantees on the accuracy of signal approximation in the Fourier domain.

SVD: Eigenfaces 1 [Python]

This video describes how the singular value decomposition (SVD) can be used to efficiently represent human faces, in the so-called "eigenfaces" (Python code, part 1).

Identifying Dominant Balance Physics from Data - Jared Callaham

This video illustrates a new algorithm to identify local dominant physical balance relations from multiscale spatiotemporal data.

Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 16 - Monte Ca...

Professor Emma Brunskill
Assistant Professor, Computer Science
Stanford AI for Human Impact Lab
Stanford Artificial Intelligence Lab
Statistical Machine Learning Group

Expressing Vectors in Different Frames Using Rotation Matrices

In this video we develop notation to express a vector in different reference/coordinate frames. We then investigate how to use rotation matrices to translate from a vector expressed in one...

Virtual Laboratory for Vibrations and Control Theory

This virtual lab developed in Matlab Simscape provides an innovative tool for learning and teaching fundamentals of mechanical vibrations including mass-spring-damper systems. 

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...

Design of Embedded Robust Control Systems Using MATLAB®/Simulink®

Robust control theory allows for changes in a system whilst maintaining stability and performance. Applications of this technique are very important for dependable embedded systems, making...