From 80869ea541b66f0db76a1d1a2aef280611cae8f3 Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Wed, 2 Oct 2024 10:50:28 -0400 Subject: [PATCH] vault backup: 2024-10-02 10:50:28 --- 4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md b/4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md index b41664f0..5f87f349 100644 --- a/4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md +++ b/4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md @@ -91,4 +91,4 @@ If this research is successful, this diffusion model will accomplish three main Perturbing a nominal plant to establish robustness is not a new technique. Robust control can find the set of plants with which a controller remains performant. Finding this set is a well understood problem, and can be straightforward. An engineer can use this set of plants to guarantee how robust a nominal controller is to perturbation. But, engineer cannot use this set to make guarantees about a implemented controller. Implementation of control laws requires lowering the abstraction level from the model of a controller to a computer program. Robustness of this controller implementation can be suggested by analysis of the model, but can be verified through experimentation. -Experimentally verifying robustness for implementations of controllers \ No newline at end of file +Experimentally verifying robustness for implementations of controllers requires elements to be extracted from the set. There are two main ways this has been done: structured and unstructured perturbations. Structured perturbations are created manually: an engineer attributes probability distributions to certain system parameters to include a margin of error. These distributions are sampled to create the perturbation. Unstructured perturbations are trickier to generate, because the perturbation form is not defined. \ No newline at end of file