vault backup: 2024-08-29 17:21:37
This commit is contained in:
parent
cef6e57367
commit
55eb76aae3
4
.obsidian/workspace.json
vendored
4
.obsidian/workspace.json
vendored
@ -180,9 +180,9 @@
|
||||
},
|
||||
"active": "3c8fa3261c26a8f5",
|
||||
"lastOpenFiles": [
|
||||
"4. Qualifying Exam/1. Managing Stuff/QE Tasks and Details.md",
|
||||
"4. Qualifying Exam/2. Writing/QE Abstract.md",
|
||||
"3-99 Research/6. Researching Techniques/Highlighting Colors and What they Mean.md",
|
||||
"4. Qualifying Exam/2. Writing/QE Abstract.md",
|
||||
"4. Qualifying Exam/1. Managing Stuff/QE Tasks and Details.md",
|
||||
"QE Abstract Take 1 Edits.md",
|
||||
"1. Daily Notes/8. August/2024-08-29.md",
|
||||
"300s School/302. NUCE 2100 - Fundamentals of Nuclear Engineering/2024-09-03 Homework 1.md",
|
||||
|
||||
@ -42,4 +42,6 @@ We can know the set of allowable plants that our controller can command, and we
|
||||
|
||||
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.
|
||||
The most common way of accomplishing this task today is by using structured perturbations, where an engineer traces throughout the system where uncertainty comes from and then establishes probability density functions for each uncertainty, culminating in a probabilistic model that can be sampled to create elements of the set.
|
||||
This is an expensive way to create perturbations.
|
||||
The other way is by using unstructured perturbations. For this method, some transfer function $\delta$ with a maximum gain is used to augment the nominal plant model.
|
||||
Loading…
x
Reference in New Issue
Block a user