Task-Preserving EEG Anonymization Using Latent Feature Masking.

Abstract

This article presents time-dependent outputfeedback and state-feedback sampled-data control strategies for achieving both state and output reachability in permanent magnet synchronous generator-based wind turbine systems using a fuzzy approach. First, a nonlinear wind turbine model is represented as a set of fuzzy linear subsystems subject to bounded disturbances and parametric uncertainty. Unlike conventional sampled-data control schemes, a unified samplingtime- dependent fuzzy control framework is developed for both state-feedback and output-feedback cases. The framework varies across sampling periods and incorporates Bernoulli random packet dropouts, thereby forming a closed-loop system. Next, the fundamental Lyapunov component is modified by incorporating aperiodic sampling with various weighting levels. A samplingvariable- dependent discontinuous Lyapunov-Krasovskii functional, combined with a fuzzy membership function-dependent $\mathcal {H}\_\infty$ technique, is employed to derive sufficient reachability conditions. Finally, the simulation results, including comparative studies with existing approaches, demonstrate the applicability of the proposed control strategies and confirm improvements in terms of allowable maximum sampling period, reduced H$\mathcal {H}\_\infty$ performance bounds, tighter reachable-set ellipsoids, and fewer decision variables.