  This paper investigates a novel finite-time gradient descent-based adaptive
neural network finite-time control strategy for the attitude tracking of a
3-DOF lab helicopter platform subject to composite disturbances. First, the
radial basis function neural network (RBFNN) is applied to estimate lumped
disturbances, where the weights, centers and widths of the RBFNN are trained
online via finite-time gradient descent algorithm. Then, a finite-time
backstepping control scheme is constructed to fulfill the tracking control of
the elevation and pitch angles. In addition, a hybrid finite-time
differentiator (HFTD) is introduced for approximating the intermediate control
signal and its derivative to avoid the problem of "explosion of complexity" in
the traditional backstepping design protocol. Moreover, the errors caused by
the HFTD can be attenuated by the combination of compensation signals. With the
aid of the stability theorem, it is proved that the closed-loop system is
semi-globally uniformly ultimately boundedness in finite time. Finally, a
comparison result is provided to illustrate the effectiveness and advantages of
the designed control strategy.
