Cyclic reformulation of LTI systems turns noise-induced parameter changes into vertices of a polytopic uncertainty model usable for robust H-infinity control synthesis from one experiment.
Interpretable deep learning for nonlinear system identification using frequency response functions with ensemble uncertainty quantification
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From Noise to Knowledge: System Identification with Systematic Polytope Construction via Cyclic Reformulation
Cyclic reformulation of LTI systems turns noise-induced parameter changes into vertices of a polytopic uncertainty model usable for robust H-infinity control synthesis from one experiment.