A Novel Deep Reinforcement Learning Approach for Dynamic Proportional-Integral Control in Scanning Probe Microscopy.

ABSTRACT: 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 red

SUBMITTER: Wei Z 

PROVIDER: S-EPMC12444835 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature