# Adaptive robotic arm control through digital twin integration and hybrid neural networks

## TL;DR

This paper introduces a new robotic arm control system using digital twins and hybrid neural networks to improve accuracy and response time in complex environments.

## Contribution

A novel three-component hybrid neural network architecture integrated with digital twin technology for adaptive robotic arm control.

## Key findings

- Achieved 98.73 ± 0.24% tracking accuracy with a UR10e robotic arm.
- Improved response time by 35.2% and disturbance rejection by 42.3% over conventional methods.
- Demonstrated generalizability across multiple robotic platforms and payload conditions.

## Abstract

This paper presents a novel digital twin-driven intelligent robotic arm adaptive control system that integrates hybrid neural network architectures to address the limitations of traditional feedback control mechanisms in complex operational environments. The proposed approach uniquely combines Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer modules through a three-way collaborative optimization mechanism, distinguishing it from existing two-component hybrid architectures. The digital twin framework establishes bidirectional closed-loop mapping between physical and virtual domains, enabling real-time predictive optimization and continuous learning capabilities. Comprehensive experimental validation using a six-degree-of-freedom UR10e robotic arm demonstrates significant performance improvements: tracking accuracy of 98.73 ± 0.24% (mean ± std, n = 30 trials), response time improvements of 35.2%, and disturbance rejection capabilities enhanced by 42.3% compared to conventional PID and computed torque control methods (p < 0.01). Additional simulations on three robotic platforms (ABB IRB 6640, KUKA KR 10, Fanuc M-20iA) confirm the generalizability of the approach. The system maintains exceptional stability across diverse payload configurations (0.5-8kg) and external disturbance scenarios, with training data efficiency superior to standalone architectures by requiring 40% fewer samples for equivalent performance.