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Dane Sabo 2024-10-16 10:17:20 -04:00
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commit ca7f2939fb
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.obsidian/plugins/obsidian-pandoc-reference-list/data.json vendored
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@ -12,5 +12,6 @@
"renderCitationsReadingMode": true,
"renderLinkCitations": true,
"pullFromZotero": true,
"cslStyleURL": "https://raw.githubusercontent.com/citation-style-language/styles/master/ieee.csl"
"cslStyleURL": "https://raw.githubusercontent.com/citation-style-language/styles/master/ieee.csl",
"enableCiteKeyCompletion": true
}

2
4 Qualifying Exam/2 Writing/2. QE State of the Art.md
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@ -20,7 +20,7 @@ Robust control as a field determines how resilient a control system is to a diff
Robustness is dependent on two features: the characteristic to be guaranteed, and the set of reasonably possible perturbed plants $\mathcal{P}$. Usually the characteristic we're interested in is internal stability or performance. The possible set of plants, however, is less straightforward. The set $\mathcal{P}$ can be structured or unstructured. A structured set in this instance can be a discrete number of possible perturbed plants, or possibly a parametric study with a finite number of parameters. Let's consider an example.
Suppose a plant representing a spring-mass-damper system is described as follows:
Suppose a plant representing a spring-mass-damper system is described as follows @controltutorialsformatlab&simulinkInvertedPendulumSystem:
$$ P = \frac{X(s)}{F(s)} = \frac{1}{ms^2 + bs +k}$$
(The disk multiplicative perturbation)