A primal-dual online framework updates policies from closed-loop data for SDP-based control synthesis in linear discrete-time systems, with local linear tracking and global ergodic convergence guarantees under persistency of excitation and slow data variation.
Synthesis of safety certificates for discrete-time uncertain systems via convex optimizati on,
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A Data-Enabled Primal-Dual Approach for Policy Learning with SDP Formulations
A primal-dual online framework updates policies from closed-loop data for SDP-based control synthesis in linear discrete-time systems, with local linear tracking and global ergodic convergence guarantees under persistency of excitation and slow data variation.