27 lines
2.6 KiB
Markdown
27 lines
2.6 KiB
Markdown
1 Page
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**TARGET: 500-600 words**
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# Outline
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1. What is the purpose of this research?
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1. Use diffusion generative diffusion models to create examples of perturbed plants
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2. You can evaluate robustness of an abstract controller, but actually testing it on real plants is more difficult.
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2. What are the outcomes?
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1. Train a diffusion generative model to generate Bode plots of dynamic systems.
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2. Use that generative model to generate perturbations of a given input plant
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3. Modulate the amount of perturbation by modulating the amount of noise used in the diffusion model
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The goal of this research is to use a generative diffusion model to create unstructured perturbations of a nominal plant. If this research is successful, this diffusion model will accomplish three main tasks:
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1. Generate Bode plots based on training data of example dynamic systems
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2. Perturb a nominal plant in an unstructured manner with a controllable difference between perturbed and nominal plants
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3. Approximate a set of controllable plants by generating a large number of perturbed examples
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1. (USE LOCATIONS OF POLES AND ZEROS TO MEASURE DISTANCE)
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# Version 1
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## Attempt
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The goal of this research is to use a generative diffusion model to create unstructured perturbations of a nominal plant. If this research is successful, this diffusion model will accomplish three main tasks:
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1. It will approximate a set of controllable plants by generating a large number of perturbed examples
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2. Perturb a nominal plant in an unstructured manner with a controllable amount of uncertainty
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3. Generate time and frequency domain responses based on training data of example systems.
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The diffusion generative model has shown great promise in creating novel and realistic samples from training data. This research will train a generative model to create Bode plots of transfer functions. This model will be given a nominal plant as an input and then generate a perturbed plant. Once created, this perturbed plant can be evaluated if it belongs to the set of controllable plants for a desired controller. This process will be repeated several times to generate enough plants to approximate the set of controllable plants.
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These generated plants can be used to verify robustness of controller implementations. A model of a controller can use robust control theory to establish the set of controllable plants, but an actual implementation of a controller can not be verified as robust in the same way. Instead, it must be verified experimentally using elements of the set. Extracting elements of the set is not a trivial task, but if this research is successful, a generative model can reduce the effort required to create perturbed plants. |