Data Informativity for Analysis and Design of Positive Systems
Résumé
This paper studies data informativity of positive systems using linear programming (LP). The concept called data informativity represents the sufficiency of a given dataset to solve analysis/design problems. We provide the necessary and sufficient conditions for the data-driven analysis and design problems of positive systems to be solvable. Moreover, we clarify that these conditions are characterized by LP problems. We provide numerical examples to demonstrate the effectiveness of our approaches.
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