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4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md
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@ -89,4 +89,6 @@ If this research is successful, this diffusion model will accomplish three main
**Outcome 3:** Generate time and frequency domain responses based on training data of example systems. The diffusion model is like any other machine learning model: it requires training data. For this diffusion model, we will create training data of physically realizable plants dynamics. This training data will teach the diffusion model to create realistic time and frequency responses as novel samples.
Perturbing a nominal plant to establish robustness is not a new technique. Robust control can find the set of plants with which a controller remains performant. Finding this set is a well understood problem, and can be straightforward. An engineer can use this set of plants to guarantee how robust a nominal controller is to perturbation. But, engineer cannot use this set to make guarantees about a implemented controller. Implementation of control laws requires lowering the abstraction level from the model of a controller used in robust control and must be verified for robustness independently.
Perturbing a nominal plant to establish robustness is not a new technique. Robust control can find the set of plants with which a controller remains performant. Finding this set is a well understood problem, and can be straightforward. An engineer can use this set of plants to guarantee how robust a nominal controller is to perturbation. But, engineer cannot use this set to make guarantees about a implemented controller. Implementation of control laws requires lowering the abstraction level from the model of a controller to a computer program. Robustness of this controller implementation can be suggested by analysis of the model, but can be verified through experimentation.
Experimentally verifying robustness for implementations of controllers