Fractional-Order Nonsingular Terminal Sliding Mode Control of a Cable-Driven Aerial Manipulator Based on RBF Neural Network

High-performance control of aerial manipulators with active interactive operation under lumped disturbance is challenging. In this article, a robust control scheme is developed for the precise and anti-disturbance motion control of a novel cable-driven aerial manipulator. The fractional-order nonsingular terminal sliding mode control in the control structure guarantees tracking accuracy and avoids singular issues. At the same time, the radial basis function (RBF) neural network is adopted to approximate and compensate for the lumped disturbance to improve the tracking accuracy and robustness of the system. Besides, the chattering phenomenon of the sliding mode control is weakened thanks to the fractional calculus and the replacement of the sign function by a novel power function. Finally, several comparative simulation cases are conducted, and the effectiveness and superiorities of the proposed control strategy are demonstrated.