Adaptive Neural Barrier Function-Based Fast Terminal Sliding Mode Control for Bionic Aerial Manipulators in Canopy Sampling

This paper proposes a novel adaptive sliding mode control strategy for bionic aerial manipulators performing canopy-sampling tasks. Specifically, an adaptive neural barrier function-based fast terminal sliding mode control (BFASMC-NN) scheme is developed to address the joint-space trajectory tracking problem by integrating fast continuous nonsingular terminal sliding mode control (FNTSMC), neural networks (NNs), and barrier functions (BFs). The aerial manipulator is modeled as a rootless system, and its kinematic and dynamic characteristics are analyzed separately. Radial basis function neural networks (RBF-NNs) are introduced to approximate lumped disturbances, while BFs are incorporated to mitigate the effects of joint input saturation. Meanwhile, FNTSMC is employed to guarantee finite-time convergence of the system states. The stability of the closed-loop system is rigorously proven based on Lyapunov stability theory. Two simulation studies are conducted to validate the proposed method, and the results demonstrate that it achieves stronger disturbance rejection capability, faster convergence, and higher tracking accuracy than existing approaches.