Hybrid-Driven Dynamic Position Prediction of Robot End-Effector Integrating Parametric Dynamic Model and Machine Learning Article Swipe
Hepeng Ni
,
Cong Xu
,
Yingxin Ye
,
Bo Chen
,
Shuangsheng Luo
,
Shuai Ji
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15020895
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15020895
Accurate dynamic model and response prediction of industrial robots (IRs) are prerequisites for production optimization before actual operation. In this study, a hybrid-driven dynamic position prediction (HDPP) approach integrating a parametric dynamic model (PDM) and learning-based residual error compensators (RECs) is developed to estimate the actual position of a robot end-effector based on the reference input trajectory. Firstly, a PDM consisting of a flexible dynamic model of the mechanical system and a servo system model is constructed as the primary predictor in HDPP. Meanwhile, a reinforcement learning (RL)-based parameter identification method is presented to obtain independent dynamic parameters, which integrates a CAD model, least squares estimation, and RL. Then, an REC based on the temporal convolutional network long short-term memory (TCN-LSTM) is proposed for each joint to compensate for the residual error after PDM prediction. A TCN is employed as the input of LSTM to extract and compress the discontinuous features, which can enhance the compensator’s accuracy and stability. Additionally, a dynamics-integrated (DI) dataset construction scheme is developed for network training to boost the prediction accuracy. Finally, a series of experiments and comparative analysis are preformed to validate the performance of HDPP in terms of prediction accuracy and stability.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app15020895
- https://www.mdpi.com/2076-3417/15/2/895/pdf?version=1737115051
- OA Status
- gold
- Cited By
- 1
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406668532
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4406668532Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app15020895Digital Object Identifier
- Title
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Hybrid-Driven Dynamic Position Prediction of Robot End-Effector Integrating Parametric Dynamic Model and Machine LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-17Full publication date if available
- Authors
-
Hepeng Ni, Cong Xu, Yingxin Ye, Bo Chen, Shuangsheng Luo, Shuai JiList of authors in order
- Landing page
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https://doi.org/10.3390/app15020895Publisher landing page
- PDF URL
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https://www.mdpi.com/2076-3417/15/2/895/pdf?version=1737115051Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/15/2/895/pdf?version=1737115051Direct OA link when available
- Concepts
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Computer science, Position (finance), Artificial intelligence, Robot end effector, Robot, Control engineering, Engineering, Finance, EconomicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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43Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2025-01-17 |
| publication_year | 2025 |
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