Adaptive Fast Terminal Sliding Mode Control for a Class of Uncertain System

Based on neural networks, an adaptive fast terminal sliding mode (FTSM) control strategy is presented for a class of high-order uncertain nonlinear system. The radial basis function (RBF) neural network is used to online approach uncertainties of system. The mathematical relationship between the neighborhood of each sliding mode surface and the system uncertainty is derived. It is strictly proved that the system tracking error can reach to a very small region in finite time, and the robustness of the controller is established using the Lyapunov stability theory. Theoretical analysis and simulation results show the good tracking performance of the designed controller.