vault backup: 2024-10-16 10:07:04

.obsidian/hotkeys.json

@@ -226,5 +226,13 @@
       ],
       "key": "D"
     }
+  ],
+  "window:reset-zoom": [
+    {
+      "modifiers": [
+        "Alt"
+      ],
+      "key": "Z"
+    }
   ]
 }

4 Qualifying Exam/2 Writing/2. QE State of the Art.md

@@ -18,7 +18,10 @@ type: single
 ## Attempt
 Robust control as a field determines how resilient a control system is to a difference in plant dynamics for a given characteristic. In a real system, there will always be some inaccuracy in the model of plant dynamics, disturbances, or other noise. These unmodeled features will affect plant behavior if they are not anticipated. Robust control gives us tools to design for these perturbations proactively. We can design characteristics such as performance and stability to guarantee as 'robust'. 
 
-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. 
+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 set of plants representing a spring-mass-damper system is described as follows:
+$$\mathcal{P} = \left{ \frac{}}
 
 (The disk multiplicative perturbation)