This paper investigates the control problem of dual-arm unmanned aerial manipulator systems (DAUAMs). Strong coupling between the dual-arm and the multirotor platform, together with unmodeled dynamics and external disturbances, poses significant challenges to stable and accurate operation. An adaptive event-triggered control scheme with neural network-based approximation is proposed to address these issues while explicitly considering communication constraints. First, a dynamic model of the DAUAM system is derived, and a command-filter-based backstepping framework with error compensation is constructed. Then, a neural network is employed to approximate external frictions, and an event-triggered mechanism is designed to reduce the transmission frequency of control updates, thereby alleviating communication and energy burdens. Lyapunov-based analysis shows that all closed-loop signals remain bounded and that the tracking error converges to a neighborhood of the desired trajectory within a fixed time. Finally, experiments on a self-built DAUAM platform demonstrate that the proposed approach achieves accurate trajectory tracking.

翻译：本文研究双臂无人飞行机械臂系统（DAUAMs）的控制问题。双臂与多旋翼平台之间的强耦合，以及未建模动态和外部干扰，给系统的稳定精确运行带来了重大挑战。针对上述问题，同时明确考虑通信约束，提出了一种基于神经网络逼近的自适应事件触发控制方案。首先，推导了DAUAM系统的动力学模型，并构建了带有误差补偿的指令滤波反步控制框架。随后，采用神经网络逼近外部摩擦力，并设计事件触发机制以降低控制指令的传输频率，从而减轻通信和能量负担。基于李雅普诺夫的分析表明，所有闭环信号均有界，且跟踪误差在固定时间内收敛至期望轨迹的邻域内。最后，在自建DAUAM平台上开展的实验证实了所提方法能够实现精确的轨迹跟踪。