Attitude tracking backstepping control for small unmanned helicopters based on adaptive neural network under unknown environment disturbances

To solve the attitude tracking control problem of small unmanned helicopters under unknown bounded disturbances, an attitude tracking backstepping controller based on adaptive radial basis function (RBF) neural network disturbance compensation is proposed in this paper. The unknown disturbance is estimated online and in real time through RBF neural network with a novel gradient descent weight update rate. A novel attitude tracking backstepping controller based on virtual control variables is designed, and the system stability is analyzed using the Lyapunov method. The experimental verification of the backstepping controller is carried out on the three-degrees-of-freedom helicopter. The experiments show the effectiveness and advancedness of the proposed adaptive neural network backstepping controller in suppressing unknown external disturbances compared with the robust adaptive integral backstepping.