  This article focuses on the globally composite adaptive law-based intelligent
finite-/fixed- time (FnT/FxT) tracking control issue for a family of uncertain
strict-feedback nonlinear systems. First, intelligent approximators with new
composite updating laws are developed to model uncertain nonlinear terms, which
encompass prediction errors to enhance intelligent approximators' learning
behaviors and fewer online learning parameters to diminish computational
burden. Then, a novel smooth switching function coupled with robust controllers
is designed to pull system states back when the transients are out of the
approximators' active domain. After that, a modified FnT/FxT backstepping
technique is constructed to render output to follow the reference trajectory,
and an adaptive law is employed to alleviate the impact of external
disturbances. Moreover, input nonlinearities are considered in the design of
control laws. In view of the known actuator magnitude and rate saturations, a
novel adaptive fast FxT auxiliary variable system is established to effectively
diminish saturation time, and further alleviate the impact of the auxiliary
signal on the tracking error. Furthermore, when the input is subject to unknown
magnitude saturation, rate saturation and dead-zone simultaneously, a smooth
function is introduced to approximate the non-smooth input nonlinearities,
where the unknown parameters are modeled via the presented global intelligent
approximators. It is theoretically confirmed that the proposed control
strategies ensure globally FnT/FxT boundedness of all the closed-loop
variables. Finally, the validity of theoretical results is testified via a
simulation case.
