Learning event-triggered controllers for linear parameter-varying systems from data
The research develops data-driven control strategies for event-triggered linear parameter-varying (LPV) systems without needing precise system models. It introduces a $\theta$-persistence of excitation condition to determine the minimum data length needed for analysis and uses Petersen's lemma to derive stability certificates via tractable semidefinite programs, enabling robust gain scheduling.