Finite-Time Neural Network Backstepping Control of an Uncertain Fractional-Order Duffing System With Input Saturation - Lv, Hui; Zhang, Xiulan (Frontiers in Physics, 2020)
In this paper, neural network (NN) control of fractional-order Duffing system (FODS) by suing backstepping method within finite-time in the presence of input saturation is investigated. A fractional-order filter with order lying on the interval (1,2) is used to estimate the virtual input together with its fractional derivative, and the estimation error tends to a small region in some finite time. Fractional-order law is designed for the parameter of NN, and an adaptive NN controller is given. The proposed method drives the tracking error tending to an arbitrary small region within finite time. The simulation results verify the validity of the proposed method.