Integral Backstepping Sliding Mode Control for Unmanned Autonomous Helicopters Based on Neural Networks

In this paper, we propose an adaptive control approach to deal with the problems of input saturation, external disturbances, and uncertainty in the unmanned autonomous helicopter system. The dynamics of the system take into account the presence of input saturation, uncertainty, and external disturbances. Auxiliary systems are built to handle the input saturation. The neural networks are applied to approximate the uncertain terms. The control scheme combining integral backstepping and sliding mode control is developed in position and attitude subsystems, respectively. In the closed-loop system, the boundedness of the signals is proved by means of the Lyapunov theory. The simulation demonstrates that the approach has good robustness and tracking performance.