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

Control Bootcamp

University of Washington
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#Engineering #Electrical Engineering #Control Systems #Signal Processing #Kalman Filter #Mathematics #Algebra #Linear Algebra #Linear Systems #Stability Analysis #Eigenvalues #Computer Science #DevOps #Observability
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Control Bootcamp: Overview