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This brief aims at the issue of globally composite-learning-based neural fastfinite-time (F-FnT) tracking control for a class of uncertain systems instrict-feedback form subject to nonlinearly periodic disturbances. First,uncertain dynamics with periodic parameters are identified by incorporatingFourier series expansion (FSE) into an intelligent estimator, which leveragesthe feedback of newly designed prediction errors in updating weights to boostlearning performance. Then, a novel switching mechanism is constructed tofulfill smooth switching from the composite FSE-based neural controller torobust control law when the inputs of the intelligent estimator transcend thevalid approximation domain. By fusing the switching mechanism with an improvedF-FnT backstepping algorithm, the globally F-FnT boundedness of all variablesin the closed-loop system is guaranteed. Finally, a simulation study isconducted to evince the availability of the theoretical result.

Globally Composite-Learning-Based Intelligent Fast Finite-Time Control for Uncertain Strict-Feedback Systems with Nonlinearly Periodic Disturbances