A Novel Deep Reinforcement Learning Approach for Dynamic Proportional‐Integral Control in Scanning Probe Microscopy Article Swipe
Ziwei Wei
,
Shiqing Wei
,
Qibin Zeng
,
Wanheng Lu
,
Huajun Liu
,
Kaiyang Zeng
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/smll.202503921
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/smll.202503921
Scanning Probe Microscopy (SPM) measurements often encounter challenges due to the nonlinear and time‐varying behavior of materials, as well as abrupt changes in structure, properties, or topography. These issues can lead to system instability and imaging artifacts. Traditional proportional‐integral (P‐I) controllers with fixed parameters struggle to adapt to such conditions. To address this limitation, this study introduces the Parallel Integrated Control and Training System (PICTS), which leverages deep reinforcement learning (DRL) to dynamically adjust control strategies in real time. This approach stabilizes probe–sample interactions, even for samples with sharp edges, soft multiphase materials, or complex topographies. Experimental results demonstrate that the DRL‐based controller reduces deflection errors by 26% – 90% compared to commercial fixed‐parameter controllers, producing more stable images with fewer artifacts. Statistical analyses confirm improved precision, with error values concentrated near zero. Additionally, the system employs a field‐programmable gate array (FPGA) for critical tasks, ensuring efficient operation without the need for high‐performance computing. By integrating advanced machine learning, this work enhances imaging quality and stability in demanding environments, paving the way for further innovations in SPM.
Related Topics
Concepts
Reinforcement learning
Computer science
Field-programmable gate array
Nonlinear system
Deflection (physics)
Scanning probe microscopy
Artificial intelligence
Controller (irrigation)
Control system
Control theory (sociology)
Materials science
Control (management)
Computer hardware
Nanotechnology
Optics
Engineering
Physics
Electrical engineering
Biology
Quantum mechanics
Agronomy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/smll.202503921
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/smll.202503921
- OA Status
- hybrid
- Cited By
- 1
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412776711
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412776711Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/smll.202503921Digital Object Identifier
- Title
-
A Novel Deep Reinforcement Learning Approach for Dynamic Proportional‐Integral Control in Scanning Probe MicroscopyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-07-30Full publication date if available
- Authors
-
Ziwei Wei, Shiqing Wei, Qibin Zeng, Wanheng Lu, Huajun Liu, Kaiyang ZengList of authors in order
- Landing page
-
https://doi.org/10.1002/smll.202503921Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/smll.202503921Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/smll.202503921Direct OA link when available
- Concepts
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Reinforcement learning, Computer science, Field-programmable gate array, Nonlinear system, Deflection (physics), Scanning probe microscopy, Artificial intelligence, Controller (irrigation), Control system, Control theory (sociology), Materials science, Control (management), Computer hardware, Nanotechnology, Optics, Engineering, Physics, Electrical engineering, Biology, Quantum mechanics, AgronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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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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29Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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