Digital Twin-Empowered Robotic Arm Control: An Integrated PPO and Fuzzy PID Approach
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 an...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/2/216 |
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| author | Yuhao Cen Jianjue Deng Ye Chen Haoxian Liu Zetao Zhong Bo Fan Le Chang Li Jiang |
| author_facet | Yuhao Cen Jianjue Deng Ye Chen Haoxian Liu Zetao Zhong Bo Fan Le Chang Li Jiang |
| author_sort | Yuhao Cen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-eba7ffa37811463c9e40428d1e13b4aa |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-eba7ffa37811463c9e40428d1e13b4aa2025-01-24T13:39:46ZengMDPI AGMathematics2227-73902025-01-0113221610.3390/math13020216Digital Twin-Empowered Robotic Arm Control: An Integrated PPO and Fuzzy PID ApproachYuhao Cen0Jianjue Deng1Ye Chen2Haoxian Liu3Zetao Zhong4Bo Fan5Le Chang6Li Jiang7School of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaBeijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaWith 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.https://www.mdpi.com/2227-7390/13/2/216robotic arm controldigital twindeep reinforcement learningproximal policy optimizationfuzzy PID |
| spellingShingle | Yuhao Cen Jianjue Deng Ye Chen Haoxian Liu Zetao Zhong Bo Fan Le Chang Li Jiang Digital Twin-Empowered Robotic Arm Control: An Integrated PPO and Fuzzy PID Approach Mathematics robotic arm control digital twin deep reinforcement learning proximal policy optimization fuzzy PID |
| title | Digital Twin-Empowered Robotic Arm Control: An Integrated PPO and Fuzzy PID Approach |
| title_full | Digital Twin-Empowered Robotic Arm Control: An Integrated PPO and Fuzzy PID Approach |
| title_fullStr | Digital Twin-Empowered Robotic Arm Control: An Integrated PPO and Fuzzy PID Approach |
| title_full_unstemmed | Digital Twin-Empowered Robotic Arm Control: An Integrated PPO and Fuzzy PID Approach |
| title_short | Digital Twin-Empowered Robotic Arm Control: An Integrated PPO and Fuzzy PID Approach |
| title_sort | digital twin empowered robotic arm control an integrated ppo and fuzzy pid approach |
| topic | robotic arm control digital twin deep reinforcement learning proximal policy optimization fuzzy PID |
| url | https://www.mdpi.com/2227-7390/13/2/216 |
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