Fast Convergence and Robustness for Two-Layered Forgetting Recursive Least Square under Finite Excitation (opens in new tab)

Under nonpersistent excitation (non-PE) conditions, conventional methods such as exponential forgetting (EF) or directional forgetting (DF) recursive least squares (RLS) that rely on direct regressor vectors exhibit inherent limitations in terms of stability guarantees for parameter errors, robustness to system changes, and convergence rates. To address these limitations, this study introduces a novel two-layer forgetting RLS (TLF-RLS) identific...