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4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md
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@ -80,7 +80,7 @@ Point: We look to generative models to accelerate perturbed plant generation.
**Fifth Paragraph: Broader Impact**
## Attempt
The goal of this research is to use a generative diffusion model to create unstructured perturbations of a nominal plant.
The goal of this research is to use a generative diffusion model to create unstructured perturbations of a nominal plant. Robust control uses perturbations to verify to what distrubances a controller is able to withstand. These perturbations are not easy to generate, and often require significant effort to create. This increases verification costs, and therein makes high assurance difficult to attain. This research suggests using a relatively new technology, the generative diffusion model, to generate perturbations. With this new technology, we aim to reduce perturbation generation effort, and reduce verification costs.
If this research is successful, this diffusion model will accomplish three main tasks:
**Outcome 1:** Approximate a set of controllable plants by generating a large number of perturbed examples. This research will use the lossy nature of the diffusion model to create the perturbation. Inference of these models is relatively cheap, while maintaining the ability to create novel samples.
@ -95,4 +95,4 @@ Experimentally verifying robustness for implementations of controllers requires
This research will utilize a diffusion model to make generation of unstructured perturbations easier. The generative diffusion model is great at creating new samples with a controlled amount of distortion compared to training data. We will use this feature of the diffusion model to generate unstructured perturbations. The diffusion model consists of two processes: a forward process that degrades inputs with noise, and a reverse process that the learned model attempts to denoise the input. This reverse process will be trained using time and frequency response data of a variety of dynamic systems, so that noise removal creates samples that look like dynamic systems. Finally, we will control the amount of perturbation by controlling how much we destruct the input of a nominal plant with noise. By introducing less noise we create smaller perturbations, while to create large perturbations we do the opposite.
Verifying implementations of controllers has been an onerous task. Perturbations take a significant effort to generate due to the manual nature of current techniques. Generative models have the potential to accelerate this perturbation generation. If successful, having cheaper access to numerous valid perturbations will make robustness verification of control system implementations more accessible.
Verifying implementations of controllers has been an onerous task. Perturbations take a significant effort to generate due to the manual nature of current techniques. Generative models have the potential to accelerate this perturbation generation. If successful, having cheaper access to numerous valid perturbations will make robustness verification of control system implementations more accessible. This is a big deal for systems where high assurance is necessary. New infrastructure projects utilize modern digital controllers that suffer from this robustness verification predicament. This research has to potential to reduce the cost of verification of these systems, and in turn, reduce the cost of new infrastructure projects while maximizing system resillience.