Control theory | Stochastic control

Robust control

In control theory, robust control is an approach to controller design that explicitly deals with uncertainty. Robust control methods are designed to function properly provided that uncertain parameters or disturbances are found within some (typically compact) set. Robust methods aim to achieve robust performance and/or stability in the presence of bounded modelling errors. The early methods of Bode and others were fairly robust; the state-space methods invented in the 1960s and 1970s were sometimes found to lack robustness, prompting research to improve them. This was the start of the theory of robust control, which took shape in the 1980s and 1990s and is still active today. In contrast with an adaptive control policy, a robust control policy is static, rather than adapting to measurements of variations, the controller is designed to work assuming that certain variables will be unknown butbounded. (Wikipedia).

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What Is Robust Control? | Robust Control, Part 1

Watch the other videos in this series: Robust Control, Part 2: Understanding Disk Margin - https://youtu.be/XazdN6eZF80 Robust Control, Part 3: Disk Margins for MIMO Systems - https://youtu.be/sac_IYBjcq0 This videos covers a high-level introduction to robust control. The goal is to get

From playlist Robust Control

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Working with Parameter Uncertainty | Robust Control, Part 4

Watch the first videos in this series: Robust Control, Part 1: What Is Robust Control? - https://youtu.be/A7wHSr6GRnc Robust Control, Part 2: Understanding Disk Margin - https://youtu.be/XazdN6eZF80 Robust Control, Part 3: Disk Margins for MIMO Systems - https://youtu.be/sac_IYBjcq0 The

From playlist Robust Control

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Understanding Disk Margin | Robust Control, Part 2

Watch the other videos in this series: Robust Control, Part 1: What Is Robust Control? - https://youtu.be/A7wHSr6GRnc Robust Control, Part 2: Understanding Disk Margin - https://youtu.be/XazdN6eZF80 Robust Control, Part 3: Disk Margins for MIMO Systems - https://youtu.be/sac_IYBjcq0 As w

From playlist Robust Control

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Disk Margins for MIMO Systems | Robust Control, Part 3

Watch the first two videos in this series: Robust Control, Part 1: What Is Robust Control? - https://youtu.be/A7wHSr6GRnc Robust Control, Part 2: Understanding Disk Margin - https://youtu.be/XazdN6eZF80 This video shows how margin can be used to assess the robustness of multi-input, mult

From playlist Robust Control

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H Infinity and Mu Synthesis | Robust Control, Part 5

This video walks through a controller design for an active suspension system. Actually, we design two controllers. For the first, we use H infinity synthesis to design a controller for a nominal plant model that will guarantee performance but not necessarily be robust to variation in the s

From playlist Robust Control

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The Step Response | Control Systems in Practice

Check out the other videos in this series: https://www.youtube.com/playlist?list=PLn8PRpmsu08pFBqgd_6Bi7msgkWFKL33b This video covers a few interesting things about the step response. We’ll look at what a step response is and some of the ways it can be used to specify design requirements f

From playlist Control Systems in Practice

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Everything You Need to Know About Control Theory

Control theory is a mathematical framework that gives us the tools to develop autonomous systems. Walk through all the different aspects of control theory that you need to know. Some of the concepts that are covered include: - The difference between open-loop and closed-loop control - How

From playlist Control Systems in Practice

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Fuzzy control of inverted pendulum

Fuzzy control of inverted pendulum, State-feedback controller is designed based on T-S fuzzy model with the consideration of system stability and performance.

From playlist Demonstrations

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What Are Reactive Systems?

Reactive Systems use a high-performance software architecture. They are resilient under stress, and their reactive design allows them to scale elastically to meet demand. The reactive design approach allows the creation of more complex, more flexible systems and forms the basis for some of

From playlist Software Engineering

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Control Bootcamp: Introduction to Robust Control

This video motivates robust control with the famous 1978 paper by John Doyle, titled "Guaranteed Margins for LQG Regulators"... Abstract: There are none. Code available at: faculty.washington.edu/sbrunton/control_bootcamp_code.zip These lectures follow Chapters 1 & 3 from: Machine le

From playlist Control Bootcamp

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Control Theory and COVID-19: Control Design

Follow on Twitter: https://twitter.com/eigensteve This video will discuss several aspects of the COVID-19 control problem, including model predictive control, robustness, and the challenge of time delays in the system. Website: https://www.eigensteve.com/ Acknowledgements: Consultation

From playlist Control Theory and COVID-19

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Control Bootcamp: Sensitivity and Robustness

Here we show that peaks in the sensitivity function result in a lack of robustness. Code available at: faculty.washington.edu/sbrunton/control_bootcamp_code.zip These lectures follow Chapters 1 & 3 from: Machine learning control, by Duriez, Brunton, & Noack https://www.amazon.com/Machi

From playlist Control Bootcamp

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Control Bootcamp: Limitations on Robustness

This video describes some of the fundamental limitations of robustness, including time delays and right-half plane zeros. Code available at: faculty.washington.edu/sbrunton/control_bootcamp_code.zip These lectures follow Chapters 1 & 3 from: Machine learning control, by Duriez, Brunto

From playlist Control Bootcamp

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Robust Control with Perception in the Loop: Towards Open-World Manipulation -Russ Tedrake

Workshop on New Directions in Reinforcement Learning and Control Topic:  Robust Control with Perception in the Loop: Towards Open-World Manipulation Speaker: Russ Tedrake Affiliation: MIT/Toyota Research Institute Date: November 7, 2019 For more video please visit http://video.ias.edu

From playlist Mathematics

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Building Robust Machine Learning Models

Modern machine learning libraries make model building look deceptively easy. An unnecessary emphasis (admittedly, annoying to the speaker) on tools like R, Python, SparkML, and techniques like deep learning is prevalent. Relying on tools and techniques while ignoring the fundamentals is th

From playlist Introduction to Robust & Adversarial AI

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Optimal control of spin systems with applications in (...) - D. Sugny - Workshop 2 - CEB T2 2018

Dominique Sugny (Univ. Bourgogne) / 05.06.2018 Optimal control of spin systems with applications in Magnetic Resonance Optimal control can be viewed as a generalization of the classical calculus of variations for problems with dynamical constraints. Optimal control was born in its modern

From playlist 2018 - T2 - Measurement and Control of Quantum Systems: Theory and Experiments

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