A Survey of Methods and Architectures for Adaptive Recalling-Enhanced Recurrent Neural Network based Predictive Control for the Nano Positioning of an Electrostatic MEMS Actuator

Abstract

Electrostatic Micro-Electro-Mechanical Systems (MEMS) actuators have emerged as fundamental components in high-precision nano-positioning applications due to their low power consumption, scalability, and rapid response characteristics. However, their inherent nonlinearities, hysteresis effects, and susceptibility to environmental disturbances present significant challenges for achieving accurate and stable control. Traditional control techniques often struggle to adapt to dynamic uncertainties and time-varying system behaviors. In recent years, deep learning-based approaches, particularly Recurrent Neural Networks (RNNs), have demonstrated promising capabilities in modeling complex temporal dependencies in nonlinear systems. This paper presents a comprehensive survey of methods and architectures focused on Adaptive Recalling-Enhanced Recurrent Neural Networks (ARE-RNNs) for predictive control of electrostatic MEMS nano-positioning systems. The concept of adaptive recalling introduces enhanced memory mechanisms that selectively retain and utilize relevant historical states, thereby improving prediction accuracy and control robustness. The survey systematically reviews recent advancements in hybrid control frameworks, learning-based predictive models, and real-time implementation strategies. Furthermore, it highlights key challenges such as computational constraints, generalization capability, and integration with physical system models. By synthesizing current research trends, this work aims to provide a consolidated understanding of ARE-RNN-based predictive control methodologies and identify future research directions for high-performance MEMS nano-positioning applications.