Adaptive Sliding Mode Tracking Control for Unmanned Autonomous Helicopters Based on Neural Networks

In this paper, an adaptive sliding mode tracking control scheme is developed for the medium-scale unmanned autonomous helicopter with system uncertainties and external unknown disturbances. A simplified mathematical model is established, which is divided into position subsystem and attitude subsystem. The uncertainty term of the system is handled by the inherent approximation ability of the neural network. The sliding model control scheme under the backstepping frame is developed for tackling disturbances. The stability of the simplified system is proved by using the Lyapunov theory, and the tracking errors are guaranteed to be uniformly bounded. Numerical simulation results show that the proposed control strategy is effective.