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Abstract Model order reduction of interval systems is crucial for simplifying comprehension and designing reliable controllers while retaining uncertainty bounds. This study introduces a hybrid order-reduction framework that combines split-merge reformulation, Padé approximation, and the mountain gazelle optimization (MGO) algorithm to develop reduced-order interval models (ROIMs).
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