System Identification for Rigid‐Flexible Coupled Dynamics Model of a Cable‐Driven Aerial Manipulator

Abstract

Aerial manipulators, integrating a quadrotor base and a multifaceted manipulator, represent a frontier in robotic innovation. This study introduces an identification methodology for the comprehensive dynamics of a cable‐driven aerial manipulator, incorporating both rigidity and flexibility. The initial section delineates the conception of an inventive cable‐driven aerial manipulator. Subsequent to the design, separate dynamics models for the quadrotor and the cable system are formulated using the Newton–Euler approach. The manipulator’s dynamics incorporate joint flexibility, resulting in a hybrid rigid‐flexible dynamics model. A synthesized dynamics model emerges upon the integration of the quadrotor and manipulator models. For the system’s identification, a backpropagation neural network is utilized. Enhancement of the neural network’s performance is achieved through an augmented butterfly optimization algorithm (BOA), which fine‐tunes thresholds and weights. This algorithm operates effectively owing to a clustering‐based competitive learning and a chaotic elite learning strategy, demonstrating adeptness in data extraction. Experimental validation confirms the superior accuracy and precision of the model derived from the algorithm herein, in comparison to two alternative methodologies. The findings underscore the algorithm’s exceptional accuracy, robustness, and stability, providing an accurate dynamic representation of the aerial manipulator.