A Novel Fuzzy Neural Network Approximator with Exponential Fast Terminal Sliding Mode
Ming He, Yunfeng Liu, Guangbin Liu, Huafeng Liu · 2008
A new learning algorithm for fuzzy neural network (FNN) systems to approximate unknown nonlinear continuous functions is proposed. The concept of exponential fast terminal sliding mode is introduced into the learning algorithm to improve approximation ability. The Lyapunov stability analysis guarantees that the approximation is stable and converges to the unknown function with improved speed. The proposed FNN approximator is then applied in the control of an unstable nonlinear system. Simulation results demonstrate that the proposed method can obtain good approximation ability and tracing control of nonlinear dynamic system.