Sparse Identification of Nonlinear Dynamics for Model Predictive Control

This lecture shows how to use sparse identification of nonlinear dynamics with control (SINDYc) with model predictive control to control nonlinear systems purely from data.

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State Space in Process Control

An overview on how we can derive a state space model from a given set of state variables and inputs, as well as an intro to deviation variables. This is part...

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RL Course by David Silver - Lecture 9: Exploration and Exploitation

An overview of multi-armed bandits, contextual bandits and Markov Decision Processes.

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Koopman Spectral Analysis (Representations)

In this video, we explore how to obtain finite-dimensional representations of the Koopman operator from data, using regression.

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Standard HW Problem #1: PID and Root Locus

A walk through of a typical homework problem using the root locus method to tune a PID controller. This is the first in what may be a series of homework style problems I'll cover. This is...

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State Space to Transfer Function

In this video we show how to transform a linear state space representation of a dynamic system to an equivalent transfer function representation. We will de...

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Singular Value Decomposition (SVD): Mathematical Overview

This video presents a mathematical overview of the singular value decomposition (SVD).

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How to Land on a Planet (and how it'll be done in the future!)

This video covers the basic ideas behind how engineers develop the algorithms that allow autonomous robots to land on other planetary bodies.

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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...

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Control Systems Lectures - Time and Frequency Domain

This lecture introduces the time and frequency domains. A very quick description of the Laplace Transform is given which will be the base of many of classical control lectures in the future...

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Control Bootcamp: Sensitivity and Complementary Sensitivity

Here we explore the sensitivity and complementary sensitivity functions, which are critical in understanding robustness and performance.

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Bode Plots of Complex Transfer Functions

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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Lectures on Adaptive Control and Learning by Tansel Yucelen

A serie of lectures on the topic of adaptive controllers.

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Solving the 2D Wave Equation

In this video, we solve the 2D wave equation. We utilize two successive separation of variables to solve this partial differential equation. Topics discuss...

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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...

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Control Bootcamp: Example Frequency Response (Bode Plot) for Spring-Mass-Da...

This video shows how to compute and interpret the Bode plot for a simple spring-mass-damper system.

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Laplace domain – tutorial 5: Inverse Laplace transform

In this video, we cover inverse Laplace transform which enables us to travel back from Laplace to the time domain. We will learn how to use simple tricks alo...

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MIT 6.S191: Introduction to Deep Learning

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...

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Frequency domain – tutorial 3: filtering (periodic signals)

In this video, we learn about filtering which enables us to manipulate the frequency content of a signal. A common filtering application is to preserve desi...

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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...

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Peter Ponders PID - KalmanFilters, Alpha-Beta-Gamma filters

A Kalman filter example with all the math is used to estimate the position, velocity and acceleration after corrupting perfect data with measurement and pro... See More

Machine Learning Control: Genetic Programming Control

This lecture discusses the use of genetic programming to manipulate turbulent fluid dynamics in experimental flow control.

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Transfer Functions in Simulink for Process Control

An introduction on deriving transfer functions from a linearized state space model via Laplace Transforms, and how we can input transfer functions into Simul...

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Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 7 - Imitation...

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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Peter Ponders PID - System Identification Advanced

System Identification of a more complex system with noise and truncation errors. This is the same open loop transfer function used in the root locus videohtt... See More