Adaptive tracking control for an unmanned autonomous helicopter using neural network and disturbance observer

In this paper, an adaptive tracking control scheme is investigated for a medium scale unmanned autonomous helicopter (UAH) with unknown external disturbances and system uncertainties to achieve improvement on the flight performance. The neural networks (NNs) are employed to compensate the system uncertainties. The second-order disturbance observers are introduced to restrain the compound disturbances which are combined with the NN approximation errors and the external disturbances. Accordingly, the tracking control law is designed for the UAH. The closed-loop stability of the whole UAH system is proved by using Lyapunov function method. Simulation results show that the developed control scheme can effectively solve the tracking control problems of UAH and certainly accomplish strong robustness with respect to the external disturbances and system uncertainties.