Adaptive neural network sliding mode control of a nonlinear two‐degrees‐of‐freedom helicopter system

Abstract

AbstractThe helicopter can play an important role in military and civil applications owing to its super maneuvering ability, which is closely related to its control system. To improve control performance, this study presents an adaptive sliding mode control strategy merging an adaptive neural network for a nonlinear two‐degrees‐of‐freedom (2‐DOF) helicopter system. By setting up the Lyapunov function, the asymptotic stability of the closed‐loop system is guaranteed, the astringency of the neural network weight renewal course is pledged, and the asymptotic attitude adjustment and trajectory tracking for the desired set point are realized. The availability of the adaptive radial basis function sliding mode control is finally verified via the simulation and real implementation on a nonlinear 2‐DOF helicopter platform.