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

2026-04-18

AI Summary

This work addresses the challenges of strong coupling, unmodeled dynamics, external disturbances, and limited communication resources in dual-arm aerial manipulator systems by proposing an integrated control strategy that combines command-filtered backstepping control, neural network approximation, and an adaptive event-triggering mechanism. The approach employs neural networks to compensate for uncertainties such as external friction, while the adaptive event-triggering scheme reduces control update frequency to conserve communication resources. Rigorous Lyapunov-based analysis guarantees that all closed-loop signals remain bounded and that the tracking error converges to a neighborhood of the desired trajectory within a fixed time. Experimental validation on a custom-built platform demonstrates that the proposed method achieves high-precision trajectory tracking while effectively balancing control performance and communication efficiency.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Multiagent Systems: Adversarial Agents
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• dual-arm unmanned aerial manipulator
• strong coupling
• unmodeled dynamics
• external disturbances
• trajectory tracking

Innovation

• neural network approximation
• event-triggered control
• dual-arm unmanned aerial manipulator
• adaptive backstepping
• fixed-time convergence