Wind Tunnel Data Analysis and Testing Considerations

This is the last video in our 3 part series on wind tunnel testing. In this video, we discuss what typical plots of wind tunnel data might look like and how to extract relevant information...

Multi-agent reinforcement learning: An overview

From the abstract:

Multi-agent systems can be used to address problems in a variety of do- mains, including robotics, distributed control, telecommunications, and economics. The complexity...

Advanced process control (APC): Theory & Applications in SAGD

This webinar is presented by Thiago Avila and covers what APC is, why we do it, examples of APC in the SAGD industry, what optimization opportunities are available, and where this technology...

Adaptive Control (Part I) — Hypersonics and the MIT Rule

This blog post introduces the algorithm that ruled the adaptive flight control system of the first manned hypersonic aircraft, the North American X-15.

What is Residual Analysis?

Residuals are differences between the one-step-predicted output from the model and the measured output from the validation data set. Thus, residuals represent the portion of the validation...

Averaging Methods in Nonlinear Dynamical Systems

Perturbation theory and in particular normal form theory has shown strong growth during the last decades. So it is not surprising that the authors have presented an extensive revision of the...

System Identification: Full-State Models with Control

This lecture provides an overview of modern data-driven regression methods for linear and nonlinear system identification, based on the dynamic mode decomposition (DMD), Koopman theory, and...

Multi-Agent Reinforcement Learning: Independent vs Cooperative Agents

From the Abstract:

Intelligent human agents exist in a cooperative social environment that facilitates learning. They learn not only by trialand -error, but also through cooperation by...

Nathan Kutz:"Data-driven Discovery of Governing Physical Laws"

Seminar by Dr.Nathan Kutz on "Data-driven Discovery of Governing Physical Laws" on 10/31/2018 CICS Seminar Series

Toys for Control Education

Teaching materials for control engineering education that run in web browsers. Speed control, position control, step response of 2nd order system, pole and impulse response, and rocket.

Dynamic Mode Decomposition (Overview)

In this video, we introduce the dynamic mode decomposition (DMD), a recent technique to extract spatio-temporal coherent structures directly from high-dimensional data. DMD has been widely...

Model Reference Adaptive Control of Satellite Spin

This example shows how to control satellite spin using model reference adaptive control (MRAC) to make the unknown controlled system match an ideal reference model. The satellite system is...

Vibrational control of nonlinear systems: Vibrational controllability and tr...

In the first part of this work, the criteria for the existence of stabilizing parametric oscillations have been derived. In the present paper, the problem of choosing the stabilizing...

System Identification: Dynamic Mode Decomposition with Control

This lecture provides an overview of dynamic mode decomposition with control (DMDc) for full-state system identification. DMDc is a least-squares regression technique based on the singular...

Kristin Pettersen Lectures on Nonlinear Control

Kristin Pettersen Lectures on Nonlinear Control, including many of the necessary mathematical tools and concepts.

Geodetic Coordinates: Computing Latitude and Longitude

In this video we show how to compute the geodetic latitude and terrestrial longitude if given the velocity north and east. This is useful for simulating a body moving over a spheroid Earth...

A Nonlinear, 6 DOF Dynamic Model of an Aircraft: the Research Civil Aircraft...

In this video we develop a dynamic model of an aircraft by describing forces and moments generated by aerodynamic, propulsion, and gravity that act on the aircraft. This video outlines the...

Data-Driven Control: Error Bounds for Balanced Truncation

In this lecture, we derive error bounds for the balanced truncation.

Randomized Singular Value Decomposition (SVD)

This video describes how to use recent techniques in randomized linear algebra to efficiently compute the singular value decomposition (SVD) for extremely large matrices.

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

Computing Euler Angles: Tracking Attitude Using Quaternions

In this video we continue our discussion on how to track the attitude of a body in space using quaternions. The quaternion method is similar to the Euler Kinematical Equations and Poisson...

Gimbal Lock in reference to the Apollo missions

A gimbal is a pivoted support that permits rotation of an object about an axis. For this reason, a set of three axes gimbals are used in spacecrafts to help with orientation attitude control...

Dynamic Mode Decomposition (Examples)

In this video, we continue to explore the dynamic mode decomposition (DMD). In particular, we look at recent methodological extensions and application areas in fluid dynamics, disease...

SVD and Alignment: A Cautionary Tale

This video describes the importance of data alignment when performing the singular value decomposition (SVD). Translations and rotations both present challenges for the SVD.

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