Adaptive Neural Networks Control for Fractional-Order Uncertain Nonlinear Systems via Command Filter Technique

This work investigates the adaptive neural networks (NNs) control for non-strict feedback fractional-order nonlinear systems (FONSs) while considering the presence of external disturbance and uncertainty. To address the issue of the “explosion of terms”, an adaptive neural controller is devised utilizing the fractional-order command filter, backstepping method and Lyapunov stability theory. The adaptive NNs controller can not only ensure the stability of the closed-loop FONSs but also drive the tracking error to converge to a neighborhood near the origin. Simulation example demonstrates the effectiveness of algorithm.