Adaptive neural backstepping control of nonlinear fractional-order systems with input quantization

This article addresses the tracking control problem of uncertain fractional-order nonlinear systems in presence input quantization and external disturbance. An adaptive backstepping scheme is proposed by combining with radial basis function (RBF) neural networks (NNs), disturbance observer (FODO), method. The RBF NNs are used to approximate unknown nonlinearities systems. FODO designed compensate for parameters. hysteresis quantizer avoid chattering that possibly appears actual application. stability controller proved Lyapunov In addition, all signals closed-loop system bounded. effectiveness method confirmed simulation results.