vault backup: 2024-08-29 17:10:15

This commit is contained in:
Dane Sabo 2024-08-29 17:10:15 -04:00
parent 467d1c9966
commit cef6e57367
2 changed files with 16 additions and 24 deletions

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@ -32,4 +32,14 @@ This method of perturbation generation can be very powerful for robust controlle
## First thoughts
1. Introduce what a perturbation set is
2. Generating unstructured perturbations is hard, but maybe we can use diffusion to do it
3.
3. Who gives a fuck about validation and verification. Why do we need to do actual samples?
## Attempt
Real world control systems do not operate on nominal plants, but instead control a physical plant that has similar but slightly different dynamics.
This discrepancy of dynamics is called a perturbation, and can affect controller performance.
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.
The field of robust control and robustness analysis establishes bounds for the allowable amount of perturbation for a given controller and set of requirements.
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.
What we cannot do is easily validate our guarantees on a real controller.
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.
The most common way of accomplishing this task today is by using structured uncertainty.