Data Informativity under Data Perturbation
The episode discusses Taira Kaminaga and Hampei Sasahara's paper, "Data Informativity under Data Perturbation." The hosts explore how this study introduces a generalized noise model to characterize data informativity, unifying analyses of exogenous disturbances and measurement noise. Key findings include developing a novel matrix S-procedure to handle non-convexity and providing conditions for quadratic stabilization without requiring high signal-to-noise ratios.