Abstract
This paper presents an adaptive neural network backstepping control design for an experimental helicopter with three rotational degrees of freedom. The helicopter system is described by a state-space model with unmodeled dynamics and external disturbances. By resorting to radial basis function neural networks with online gradient descent training, such unmodeled dynamics and external disturbances can be compensated in real time. With the help of Lyapunov stability criteria, it is proved that the desired trajectories can be boundedly tracked by the corresponding attitude angles of the helicopter under the proposed adaptive neural network backstepping controller. Compared with robust adaptive integral backstepping technique, our approach has smaller root mean squared error. Finally, four sets of contrastive experiment results are, respectively, given to show the effectiveness and advantages of the proposed control scheme.
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