vault backup: 2024-10-30 15:07:24

201 Metadata/My Library.bib

@@ -12622,6 +12622,22 @@ Subject\_term: Careers, Politics, Policy},
   pages = {681--688}
 }
 
+@article{wenFeedbackLinearizationControl2024,
+  title = {Feedback Linearization Control for Uncertain Nonlinear Systems via Generative Adversarial Networks},
+  author = {Wen, Nuan and Liu, Zhenghua and Wang, Weihong and Wang, Shaoping},
+  date = {2024-03-01},
+  journaltitle = {ISA Transactions},
+  shortjournal = {ISA Transactions},
+  volume = {146},
+  pages = {555--566},
+  issn = {0019-0578},
+  doi = {10.1016/j.isatra.2023.12.033},
+  url = {https://www.sciencedirect.com/science/article/pii/S001905782300592X},
+  urldate = {2024-10-30},
+  abstract = {This article presents a novel approach to leverage generative adversarial networks(GANs) techniques to learn a feedback linearization controller(FLC) for a class of uncertain nonlinear systems. By estimating uncertainty through the adversarial process, where ground truth samples are exclusively obtained from a predefined integral model, the feedback linearization controller, learned through a minimax two-player optimization framework, enhances the reference tracking performance of the input-output uncertain nonlinear system. Furthermore, we provide theoretical guarantee of convergence and stability, demonstrating the safe recovery of robust FLC. We also address the common challenge of mode collapse in GANs training through the strict convexity of our synthesized generator structure and an enhanced adversarial loss. Comprehensive simulations and practical experiments are conducted to underscore the superiority and efficacy of our proposed approach.},
+  keywords = {Convex optimization,Feedback linearization,Generative adversarial networks,Nonlinear systems}
+}
+
 @online{wengAutoencoderBetaVAE2018,
   title = {From {{Autoencoder}} to {{Beta-VAE}}},
   author = {Weng, Lilian},
@@ -13110,6 +13126,24 @@ Subject\_term: Careers, Politics, Policy},
   file = {/home/danesabo/Zotero/storage/7RV26D7X/Zhao et al. - 2021 - Neural Lyapunov Control for Power System Transient.pdf}
 }
 
+@article{zhaoRobustVoltageControl2020,
+  title = {Robust {{Voltage Control Considering Uncertainties}} of {{Renewable Energies}} and {{Loads}} via {{Improved Generative Adversarial Network}}},
+  author = {Zhao, Qianyu and Liao, Wenlong and Wang, Shouxiang and Pillai, Jayakrishnan Radhakrishna},
+  date = {2020-11},
+  journaltitle = {Journal of Modern Power Systems and Clean Energy},
+  volume = {8},
+  number = {6},
+  pages = {1104--1114},
+  issn = {2196-5420},
+  doi = {10.35833/MPCE.2020.000210},
+  url = {https://ieeexplore.ieee.org/abstract/document/9275598},
+  urldate = {2024-10-30},
+  abstract = {The fluctuation of output power of renewable energies and loads brings challenges to the scheduling and operation of the distribution network. In this paper, a robust voltage control model is proposed to cope with the uncertainties of renewable energies and loads based on an improved generative adversarial network (IGAN). Firstly, both real and predicted data are used to train the IGAN consisting of a discriminator and a generator. The noises sampled from the Gaussian distribution are fed to the generator to generate a large number of scenarios that are utilized for robust voltage control after scenario reduction. Then, a new improved wolf pack algorithm (IWPA) is presented to solve the formulated robust voltage control model, since the accuracy of the solutions obtained by traditional methods is limited. The simulation results show that the IGAN can accurately capture the probability distribution characteristics and dynamic nonlinear characteristics of renewable energies and loads, which makes the scenarios generated by IGAN more suitable for robust voltage control than those generated by traditional methods. Furthermore, IWPA has a better performance than traditional methods in terms of convergence speed, accuracy, and stability for robust voltage control.},
+  eventtitle = {Journal of {{Modern Power Systems}} and {{Clean Energy}}},
+  keywords = {Distribution networks,Gallium nitride,generative adversarial network,Generative adversarial networks,Load modeling,Power system stability,Robust voltage control,uncertainty,Uncertainty,Voltage control,wolf pack algorithm},
+  file = {/home/danesabo/Zotero/storage/2MACG5H9/Zhao et al. - 2020 - Robust Voltage Control Considering Uncertainties of Renewable Energies and Loads via Improved Genera.pdf}
+}
+
 @article{zhaoStabilityL2gainControl2008,
   title = {On Stability, {{L2-gain}} and {{H}}∞ Control for Switched Systems},
   author = {Zhao, Jun and Hill, David J.},

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

@@ -45,4 +45,3 @@ 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. 
-

4 Qualifying Exam/2 Writing/3. QE Research Approach.md

@@ -39,5 +39,8 @@ Something to justify, why diffusion model as opposed to other generative AI
 16. Well we train a neural network as a denoiser.
 17. Because the diffusion model forward steps are small and gaussian, we can know the reverse step is also a gaussian distribution. 
 18. So for our neural network, what we're trying to learn is the mean and standard deviation of the reverse steps for a given timestep. 
+19. What does this mean for 
 
 ## Writin some stuff
+
+The purpose of this proposal is to suggest that using a generative network to create unstructured perturbations can be a viable way to advance the state of the art. But to do this, the current state of diffusion models and their place must be introduced. The generative diffusion model is a recent breakthrough in generative models. Diffusion models