vault backup: 2024-08-29 17:10:15
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.obsidian/workspace.json
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28
.obsidian/workspace.json
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@ -223,7 +206,6 @@
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@ -32,4 +32,14 @@ This method of perturbation generation can be very powerful for robust controlle
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## First thoughts
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1. Introduce what a perturbation set is
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2. Generating unstructured perturbations is hard, but maybe we can use diffusion to do it
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3.
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3. Who gives a fuck about validation and verification. Why do we need to do actual samples?
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## Attempt
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Real world control systems do not operate on nominal plants, but instead control a physical plant that has similar but slightly different dynamics.
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This discrepancy of dynamics is called a perturbation, and can affect controller performance.
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The amount of perturbation that a control system can tolerate without violating performance or safety requirements is a crucial property to understand for high assurance systems.
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The field of robust control and robustness analysis establishes bounds for the allowable amount of perturbation for a given controller and set of requirements.
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We can know the set of allowable plants that our controller can command, and we can make guarantees about plants within those sets using robust control.
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What we cannot do is easily validate our guarantees on a real controller.
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While we have been able to find the set of perturbed plants for decades, being able to create and test a controller with elements from that set is by no means trivial.
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The most common way of accomplishing this task today is by using structured uncertainty.
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