YoVDO

Control Bootcamp

Offered By: University of Washington via YouTube

Tags

Electrical Engineering Courses Control Systems Courses Kalman Filter Courses Linear Systems Courses Stability Analysis Courses Eigenvalues Courses Observability Courses

Course Description

Overview

This course provides a rapid overview of optimal control (controllability, observability, LQR, Kalman filter, etc.). It is not meant to be an exhaustive treatment, but instead provides a high-level overview of some of the main approaches, applied to simple examples in Matlab.

These lectures follow Chapter 8 from: "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz


Syllabus

Control Bootcamp: Overview.
Linear Systems [Control Bootcamp].
Stability and Eigenvalues [Control Bootcamp].
Linearizing Around a Fixed Point [Control Bootcamp].
Controllability [Control Bootcamp].
Controllability, Reachability, and Eigenvalue Placement [Control Bootcamp].
Controllability and the Discrete-Time Impulse Response [Control Bootcamp].
Degrees of Controllability and Gramians [Control Bootcamp].
Controllability and the PBH Test [Control Bootcamp].
Cayley-Hamilton Theorem [Control Bootcamp].
Reachability and Controllability with Cayley-Hamilton [Control Bootcamp].
Inverted Pendulum on a Cart [Control Bootcamp].
Pole Placement for the Inverted Pendulum on a Cart [Control Bootcamp].
Linear Quadratic Regulator (LQR) Control for the Inverted Pendulum on a Cart [Control Bootcamp].
Motivation for Full-State Estimation [Control Bootcamp].
Control Bootcamp: Observability.
Control Bootcamp: Full-State Estimation.
The Kalman Filter [Control Bootcamp].
Control Bootcamp: Observability Example in Matlab.
Control Bootcamp: Observability Example in Matlab (Part 2).
Control Bootcamp: Kalman Filter Example in Matlab.
Control Bootcamp: Linear Quadratic Gaussian (LQG).
Control Bootcamp: LQG Example in Matlab.
Control Bootcamp: Introduction to Robust Control.
Control Bootcamp: Three Equivalent Representations of Linear Systems.
Control Bootcamp: Example Frequency Response (Bode Plot) for Spring-Mass-Damper.
Control Bootcamp: Laplace Transforms and the Transfer Function.
Control Bootcamp: Benefits of Feedback on Cruise Control Example.
Control Bootcamp: Benefits of Feedback on Cruise Control Example (Part 2).
Control Bootcamp: Cruise Control Example with Proportional-Integral (PI) control.
Control Bootcamp: Sensitivity and Complementary Sensitivity.
Control Bootcamp: Sensitivity and Complementary Sensitivity (Part 2).
Control Bootcamp: Loop shaping.
Control Bootcamp: Loop Shaping Example for Cruise Control.
Control Bootcamp: Sensitivity and Robustness.
Control Bootcamp: Limitations on Robustness.
Control Bootcamp: Cautionary Tale About Inverting the Plant Dynamics.
Control systems with non-minimum phase dynamics.
Control Theory and COVID-19.
Control Theory and COVID-19: Sensors.
Control Theory and COVID-19: Summary.
Control Theory and COVID-19: Models.
Control Theory and COVID-19: Control Design.
Reinforcement Learning: Machine Learning Meets Control Theory.
Deep Reinforcement Learning: Neural Networks for Learning Control Laws.
Model Predictive Control.
Deep Reinforcement Learning for Fluid Dynamics and Control.


Taught by

Steve Brunton

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