Adaptive backstepping sliding mode controller for flight simulator based on RBFNN

For flight simulator system, a kind of Adaptive Backstepping Sliding Mode Controller (ABSMC) based on Radial Base Function Neural Network (RBFNN) observer is presented. The sliding mode control theory is famous by its characteristic that it is insensitive to the external disturbances and parameters uncertainties. Combining this characteristic with Backstepping method can simplifies the controller design. And the addition of the terminal attractor can make the arrival time shorten greatly. However, too large external disturbances and parameters uncertainties are still not allowed to the system, and the design process of ABSMC does not have the upper bound information of disturbance until a RBFNN observer is designed to solve the problems. The simulation results show that the proposed scheme can improve the tracking precision and reduce the chattering of the control input, and the system has a higher robustness.