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This paper investigates a novel finite-time gradient descent-based adaptiveneural network finite-time control strategy for the attitude tracking of a3-DOF lab helicopter platform subject to composite disturbances. First, theradial basis function neural network (RBFNN) is applied to estimate lumpeddisturbances, where the weights, centers and widths of the RBFNN are trainedonline via finite-time gradient descent algorithm. Then, a finite-timebackstepping control scheme is constructed to fulfill the tracking control ofthe elevation and pitch angles. In addition, a hybrid finite-timedifferentiator (HFTD) is introduced for approximating the intermediate controlsignal and its derivative to avoid the problem of "explosion of complexity" inthe traditional backstepping design protocol. Moreover, the errors caused bythe HFTD can be attenuated by the combination of compensation signals. With theaid of the stability theorem, it is proved that the closed-loop system issemi-globally uniformly ultimately boundedness in finite time. Finally, acomparison result is provided to illustrate the effectiveness and advantages ofthe designed control strategy.

Finite-Time Gradient Descent-Based Adaptive Neural Network Finite-Time Control Design for Attitude Tracking of a 3-DOF Helicopter