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This article focuses on the globally composite adaptive law-based intelligentfinite-/fixed- time (FnT/FxT) tracking control issue for a family of uncertainstrict-feedback nonlinear systems. First, intelligent approximators with newcomposite updating laws are developed to model uncertain nonlinear terms, whichencompass prediction errors to enhance intelligent approximators' learningbehaviors and fewer online learning parameters to diminish computationalburden. Then, a novel smooth switching function coupled with robust controllersis designed to pull system states back when the transients are out of theapproximators' active domain. After that, a modified FnT/FxT backsteppingtechnique is constructed to render output to follow the reference trajectory,and an adaptive law is employed to alleviate the impact of externaldisturbances. Moreover, input nonlinearities are considered in the design ofcontrol laws. In view of the known actuator magnitude and rate saturations, anovel adaptive fast FxT auxiliary variable system is established to effectivelydiminish saturation time, and further alleviate the impact of the auxiliarysignal on the tracking error. Furthermore, when the input is subject to unknownmagnitude saturation, rate saturation and dead-zone simultaneously, a smoothfunction is introduced to approximate the non-smooth input nonlinearities,where the unknown parameters are modeled via the presented global intelligentapproximators. It is theoretically confirmed that the proposed controlstrategies ensure globally FnT/FxT boundedness of all the closed-loopvariables. Finally, the validity of theoretical results is testified via asimulation case.

Globally Intelligent Adaptive Finite-/Fixed- Time Tracking Control for Strict-Feedback Nonlinear Systems via Composite Learning Approaches