A Comprehensive Review of Adaptive Recalling-Enhanced Recurrent Neural Network based Predictive Control for the Nano Positioning of an Electrostatic MEMS Actuator

Abstract

Nano positioning of electrostatic MEMS actuators has emerged as a critical requirement in high-precision applications such as biomedical instrumentation, nanolithography, and micro-robotics. However, nonlinear electrostatic forces, pull-in instability, hysteresis, and environmental disturbances significantly degrade positioning accuracy and robustness. Conventional control strategies often struggle to handle these complexities due to limited adaptability and poor temporal dependency modeling. In this context, adaptive recalling-enhanced recurrent neural network based predictive control has gained considerable attention as a promising solution. This review presents a comprehensive analysis of recent advancements in integrating recurrent neural networks, particularly enhanced architectures with adaptive memory recalling mechanisms, into predictive control frameworks for MEMS nano positioning. The study examines how these models effectively capture nonlinear dynamics, temporal dependencies, and system uncertainties, enabling improved tracking accuracy and stability. Furthermore, the review highlights key methodologies, datasets, evaluation metrics, and implementation challenges associated with these approaches. Comparative insights are provided to understand the performance improvements over traditional control techniques such as PID and model predictive control. The paper also discusses open research challenges, including real-time implementation constraints, model generalization, and robustness under varying operational conditions. This review aims to provide a consolidated foundation for researchers and practitioners working toward intelligent control solutions for next-generation MEMS systems.