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Simulations run with three main controllers, and the results illustrate that the proposed method may achieve good performance under the nominal and model uncertainty cases.
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In the light of these results, it is argued that the nonparametric approach can deal with a certain type of model uncertainty, in this case the presence of unknown terms of higher or lower order in the governing differential equation, but that certain expectations about the system such as the average modal density may conflict with these results.
It was observed that the random matrix theory can be used to quantify damping model uncertainty in some cases.
Hence we recommend uncertainty analysis as a proactive solution when faced with model uncertainty, which is the case for biofuel process development research.
Versions of the approach are described for the case of a nominal model and the case considering model uncertainty.
Conversely, the robustness of the sliding mode controller makes it capable of providing good tracking even in cases of model uncertainty.
A non-dominated multiple priors approach to model uncertainty (ambiguity) leads to worst-case good-deal bounds.
Climate model uncertainty in impact assessments for agriculture: A multi‐ensemble case study on maize in sub‐Saharan Africa.
Paper: "Climate model uncertainty in impact assessments foragriculture: A multi-ensemble case study on maize insub-Saharan Africa".
Paper: "Climate model uncertainty in impact assessments for agriculture: A multi-ensemble case study on maize in sub-Saharan Africa".
All of the cases are tested with varying degrees of model uncertainty to ensure that robust estimation is achieved.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com