Digital Twin-Empowered Robotic Arm Control: An Integrated PPO and Fuzzy PID Approach
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".