  This paper focuses on excitation collection and composite learning adaptive
control design for uncertain nonlinear systems. By adopting the spectral
decomposition technique, a linear regression equation is constructed to collect
previously appeared excitation information, establishing a relationship between
unknown parameters and the system's historical data. A composite learning term,
developed using the linear regression equation, is incorporating into the
Lyapunov-based parameter update law. In comparison to the existing results, all
spectrums of previously appeared excitation information are collected, with the
matrices in linear regression equation guaranteed to be bounded. This paper
introduces concepts of excited and unexcited subspaces for analyzing the
parameter estimation errors, and a novel Lyapunov function is developed for
stability analysis. It is demonstrated that, without imposing any excitation
condition, the state and excited parameter estimation error component converge
to zero, while the unexcited component remains unchanged.
