Adaptive Neural Backstepping Control of Nonlinear Fractional-Order Systems with Input Quantization

Abstract This article addresses the tracking control problem of uncertain fractional-order nonlinear systems in the presence of input quantization and external disturbance by combining with radial basis function(RBF) neural networks(NNs), fractional-order disturbance observer(FODO) and backstepping method. The unknown nonlinearities of fractional-order systems is approximated by RBF NNs. The design of hysteretic quantizer achieves quantification of input signal and avoids chattering. The FODO is utilized to evaluate the external disturbance exist in fractional-order systems. According to fractioanlorder Lyapunov stability analysis, the bounds of all the signals in the closedloop system is proved. The effectiveness of the proposed method is confirmed by the simulation results.