38 lines
3.0 KiB
XML
38 lines
3.0 KiB
XML
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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
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<channel>
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<title>Websites on My digital brain</title>
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<link>/websites/</link>
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<description>Recent content in Websites on My digital brain</description>
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<generator>Hugo -- gohugo.io</generator>
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<language>en</language>
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<atom:link href="/websites/index.xml" rel="self" type="application/rss+xml" />
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<item>
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<title>Control Bootcamp</title>
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<link>/websites/control_bootcamp/</link>
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<pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
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<guid>/websites/control_bootcamp/</guid>
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<description>Tags :
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https://www.youtube.com/playlist?list=PLMrJAkhIeNNR20Mz-VpzgfQs5zrYi085m
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Overview Linear Systems Stability and Eigenvalues Linearizing Around a Fixed Point Controllability Controllability, Reachability, and Eigenvalue Placement Controllability and the Discrete-Time Impulse Response Degrees of Controllability and Gramians Controllability and the PBH Test Cayley-Hamilton Theorem Reachability and Controllability with Cayley-Hamilton Inverted Pendulum on a Cart Eigenvalue Placement for the Inverted Pendulum on a Cart Linear Quadratic Regulator (LQR) Control for the Inverted Pendulum on a Cart Motivation for Full-State Estimation Observability Full-State Estimation Kalman Filter Observability Example in Matlab Observability Example in Matlab (Part 2) Kalman Filter Example in Matlab Linear Quadratic Gaussian (LQG) LQG Example in Matlab Introduction to Robust Control Three Equivalent Representations of Linear Systems Example Frequency Response (Bode Plot) for Spring-Mass-Damper Laplace Transforms and the Transfer Function Benefits of Feedback on Cruise Control Example Benefits of Feedback on Cruise Control Example (Part 2) Cruise Control Example with Proportional-Integral (PI) control Sensitivity and Complementary Sensitivity Sensitivity and Complementary Sensitivity (Part 2) Loop shaping Loop Shaping Example for Cruise Control Sensitivity and Robustness Limitations on Robustness Cautionary Tale About Inverting the Plant Dynamics Control systems with non-minimum phase dynamics &lt;.</description>
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</item>
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<item>
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<title>Data-Driven Dynamical Systems with Machine Learning</title>
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<link>/websites/data_driven_dynamical_systems_with_machine_learning/</link>
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<pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
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<guid>/websites/data_driven_dynamical_systems_with_machine_learning/</guid>
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<description>Tags :
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Data-Driven Control Overview Challenges With modern control (LQR, LQG, H-Infinity), we work with linear system (or linearized systems) and we develop a control law that minimize some cost function.
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Challenging systems where modern control is not efficient:
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Non-linear systems System with unknown dynamics High dimensional systems Limited measurements or control inputs For these kinds of systems, data-driven control seems to be a good alternative.
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What is control?</description>
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</item>
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</channel>
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</rss>
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