  This brief aims at the issue of globally composite-learning-based neural fast
finite-time (F-FnT) tracking control for a class of uncertain systems in
strict-feedback form subject to nonlinearly periodic disturbances. First,
uncertain dynamics with periodic parameters are identified by incorporating
Fourier series expansion (FSE) into an intelligent estimator, which leverages
the feedback of newly designed prediction errors in updating weights to boost
learning performance. Then, a novel switching mechanism is constructed to
fulfill smooth switching from the composite FSE-based neural controller to
robust control law when the inputs of the intelligent estimator transcend the
valid approximation domain. By fusing the switching mechanism with an improved
F-FnT backstepping algorithm, the globally F-FnT boundedness of all variables
in the closed-loop system is guaranteed. Finally, a simulation study is
conducted to evince the availability of the theoretical result.
