Recent Advances in Adaptive Recalling-Enhanced Recurrent Neural Network based Predictive Control for the Nano Positioning of an Electrostatic MEMS Actuator: A Systematic Review

Abstract

The increasing demand for ultra-precise positioning systems in
nanotechnology, biomedical instrumentation, and microfabrication has
significantly accelerated research in microelectromechanical systems
(MEMS) actuators. Electrostatic MEMS actuators, known for their fast
response and low power consumption, face inherent challenges such as
nonlinear dynamics, hysteresis, and environmental disturbances that
limit their positioning accuracy. In recent years, adaptive recalling
enhanced recurrent neural networks have emerged as a promising
approach to address these challenges by integrating memory-driven
learning with predictive control frameworks. This systematic review
presents a comprehensive analysis of recent advances in adaptive
recalling-enhanced recurrent neural network-based predictive control
strategies for nano positioning applications. The study examines various
architectures, including long short-term memory and gated recurrent
units, combined with adaptive recalling mechanisms to improve
temporal dependency modeling and robustness. Additionally, the review
explores advancements in control strategies such as model predictive
control and hybrid learning-based controllers tailored for MEMS
systems. Key contributions from recent literature are synthesized to
highlight improvements in tracking accuracy, disturbance rejection, and
computational efficiency. The findings indicate that adaptive recalling
mechanisms significantly enhance predictive capabilities, enabling
precise and stable nano positioning. This review also identifies existing
research gaps and outlines future directions for developing more
efficient, scalable, and real-time control systems for MEMS actuators.