vault backup: 2024-10-22 09:14:54

.obsidian/graph.json

@@ -67,6 +67,6 @@
   "repelStrength": 12.5,
   "linkStrength": 1,
   "linkDistance": 140,
-  "scale": 0.38696207104985214,
+  "scale": 0.2579747140332347,
   "close": true
 }

.obsidian/plugins/colored-tags/data.json

@@ -149,7 +149,8 @@
     "structured-uncertainty": 137,
     "Transfer-functions": 138,
     "unstructured-uncertainty": 139,
-    "visualized-design": 140
+    "visualized-design": 140,
+    "read": 141
   },
   "_version": 3
 }

200 Library Papers/farzanRobustControlSynthesis2020.md

@@ -1,15 +1,30 @@
 ---
-readstatus: true
-dateread: 
-title: Robust Control Synthesis and Verification for Wire-Borne Underactuated Brachiating Robots Using Sum-of-Squares Optimization
+readstatus: false
+dateread:
+title: "Robust Control Synthesis and Verification for Wire-Borne Underactuated Brachiating Robots Using Sum-of-Squares Optimization"
 year: 2020
 authors:
-  - Farzan, Siavash
-  - Hu, Ai-Ping
-  - Bick, Michael
-  - Rogers, Jonathan
-citekey: farzanRobustControlSynthesis2020
+
+  
+  - "Farzan, Siavash"
+  
+  - "Hu, Ai-Ping"
+  
+  - "Bick, Michael"
+  
+  - "Rogers, Jonathan"
+  
+
+citekey: "farzanRobustControlSynthesis2020"
+
+
+
+
+
+
+
 pages: 7744-7751
+
 ---
 # Indexing Information
 ## DOI
@@ -17,7 +32,7 @@ pages: 7744-7751
 ## ISBN
 [](https://www.isbnsearch.org/isbn/)
 ## Tags:
-#Actuators, #Cable-TV, #Feedback-control, #Optimization, #Parametric-statistics, #Trajectory, #Uncertainty
+#Trajectory, #Uncertainty, #Actuators, #Cable-TV, #Feedback-control, #Optimization, #Parametric-statistics, #read
 
 >[!Abstract]
 >Control of wire-borne underactuated brachiating robots requires a robust feedback control design that can deal with dynamic uncertainties, actuator constraints and unmeasurable states. In this paper, we develop a robust feedback control for brachiating on flexible cables, building on previous work on optimal trajectory generation and time-varying LQR controller design. We propose a novel simplified model for approximation of the flexible cable dynamics, which enables inclusion of parametric model uncertainties in the system. We then use semidefinite programming (SDP) and sum-of-squares (SOS) optimization to synthesize a time-varying feedback control with formal robustness guarantees to account for model uncertainties and unmeasurable states in the system. Through simulation, hardware experiments and comparison with a time-varying LQR controller, it is shown that the proposed robust controller results in relatively large robust backward reachable sets and is able to reliably track a pre-generated optimal trajectory and achieve the desired brachiating motion in the presence of parametric model uncertainties, actuator limits, and unobservable states.
@@ -36,6 +51,18 @@ pages: 7744-7751
 > >[!note] Note
 > >I don't think any of this actually means anything about being 'Robust'. They're saying the backwards sets are 'robust' but what does that even mean..? There is no mention of plant perturbation at all.
 
-### Imported: 2024-10-16 10:48 am
+>[!done] Quote
+> *The performance of the robust SOS-based controller as well as the inner-approximation of its backward reachable set are validated by 20 simulation trials of the brachiating robot attached to the full-cable model. The stiffness of the cable is set to 20% less than the nominal value.*
+> 
+> >[!note] Note
+> >Only one plant perturbation and it's structured...
+
+>[!attention] Highlight
+> *The robot starts from random initial conditions on the cable within the verified set of initial condition*
+> 
+> >[!note] Note
+> >Robust to initial conditions, but not to actual plant perturbations.
+
+### Imported: 2024-10-22 9:12 am
 
 

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

@@ -44,4 +44,5 @@ This is useful for us. If we can find an uncertainty transfer function $W_2$ tha
 
 $\Delta$ is almost always considered a free variable transfer function. Since $||\Delta||_\infty < 1 \text{ } \forall \omega$, $\Delta$ will not decrease the minimum robustness margin. This is fine for developing a controller, but when it comes to actually verifying robustness of a controller implementation, $\Delta$ cannot be a variable. To create a plant to simulate a perturbed plant, $\Delta$ must have an expression. 
 
-**Limitation**: *There is no current method for creating random examples of $\Delta$.* Because of this, it is not currently possible to test implementations of controllers against unstructured perturbations. 
+**Limitation**: *There is no current method for creating random examples of $\Delta$.* Because of this, it is not currently possible to test implementations of controllers against unstructured perturbations. 
+

4 Qualifying Exam/3 Notes/How is robust control validation done?.md

@@ -1,2 +1,12 @@
-## Reading Some Papers
-### [[farzanRobustControlSynthesis2020]]
+Short answer, it isn't. 
+
+There's a lot of papers that do 'robust control synthesis and verification' but the verification is actually formal methods
+
+1. [[farzanRobustControlSynthesis2020]] 
+	1. Uses reach tubes to claim robustness. 
+	2. Verification is several trials with one single plant perturbation. 
+	3. Different initial conditions for sure, but the plant is only changed once, and is actually a structured perturbation (20% less stiffness)
+
+I hate robot people. They use the word robust too softly.
+
+2.