Abstract
In this paper, a nonlinear adaptive control is proposed to address the trajectory tracking problem of a small-scale unmanned helicopter which is subject to model uncertainties and external disturbances. An adaptive backstepping (AB) controller is developed for the attitude model and a radial basis function neural network (RBFNN) is proposed to compensate the model uncertainties and external disturbances. In addition, a novel learning algorithm is used in the RBFNN which has the least adaptive parameters need to be adjusted. Moreover, the position controller is designed using the nonlinear dynamic inversion (NDI) technique to facilitate the implementation of the whole control system. At last, the effectiveness and the robustness of the proposed strategy are exhibited through simulation compared with the traditional PID control.
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