Digital Twin-Empowered Robotic Arm Control: An Integrated PPO and Fuzzy PID Approach

Yuhao Cen, Jianjue Deng, Ye Chen, Haoxian Liu, Zetao Zhong, Bo Fan, Le Chang and Li Jiang ()
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Yuhao Cen: School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Jianjue Deng: School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Ye Chen: School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Haoxian Liu: School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Zetao Zhong: School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Bo Fan: Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China
Le Chang: School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Li Jiang: School of Automation, Guangdong University of Technology, Guangzhou 510006, China

Mathematics, 2025, vol. 13, issue 2, 1-22

Abstract: With rapid advancements in digital twin technology within the Industrial Internet of Things, integrating digital twins with industrial robotic arms presents a promising direction. This integration promotes the remote operation and intelligence of industrial control processes. However, the control and error management of robotic arms in digital twin systems pose challenges. In this paper, we present a digital twin-empowered robotic arm system and propose a control policy using deep reinforcement learning, specifically the proximal policy optimization approach. The construction and functionality of each subsystem within the digital twin-empowered robotic arm control system are detailed. To address errors caused by mechanical structure and virtual–real mapping in the digital twin, an integrated proximal policy optimization and fuzzy PID approach is proposed. Experimental results demonstrate that proximal policy optimization is adaptable to virtual–real mapping errors, while the fuzzy PID method corrects physical errors quickly and accurately. The robotic arm can reach the target point using this integrated approach. Overall, error management problems in digital systems have been well addressed, and our scheme can provide an accurate and adaptive control strategy for the robotic arm.

Keywords: robotic arm control; digital twin; deep reinforcement learning; proximal policy optimization; fuzzy PID (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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