Neural Network-Based Adaptive Event-Triggered Control for Dual-Arm Unmanned Aerial Manipulator Systems

TL;DR

This paper presents an adaptive neural network-based event-triggered control scheme for dual-arm unmanned aerial manipulators, improving stability, accuracy, and communication efficiency under disturbances.

Contribution

It introduces a novel control framework combining neural network approximation and event-triggered mechanisms for DAUAMs, addressing coupling, disturbances, and communication constraints.

Findings

The control scheme ensures bounded signals and convergence to a neighborhood of the desired trajectory.

Experiments demonstrate accurate trajectory tracking on a self-built DAUAM platform.

The approach reduces control transmission frequency, saving communication and energy.

Abstract

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.