Adaptive AI-Based Predictive Control Techniques for Electrostatic MEMS Nano-Positioning Applications

Abstract

The precise nano-scale positioning of electrostatic microelectromechanical systems (MEMS) actuators represents a fundamental challenge in modern microsystem engineering, where sub-nanometer accuracy, nonlinear dynamic behavior, and pull-in instability collectively demand advanced control strategies beyond the capability of classical approaches. This paper presents a comprehensive review of artificial intelligence (AI) techniques applied to adaptive recalling-enhanced recurrent neural network (RNN) based predictive control frameworks for nano positioning of electrostatic MEMS actuators. The survey systematically examines the intersection of machine learning, deep learning, and intelligent control paradigms, with particular emphasis on long short-term memory (LSTM), echo state networks (ESN), gated recurrent units (GRU), and hybrid AI architectures that incorporate memory-augmented learning for real-time predictive compensation. Thirty landmark studies published are critically reviewed, covering model predictive control (MPC) integration, nonlinear system identification, hyperparameter optimization, and transfer learning strategies specific to MEMS dynamics. A comparative analysis table is provided to highlight methodological evolution, performance benchmarks, and contributions across reviewed works. The paper further discusses current trends in federated learning for distributed MEMS control, physics-informed neural networks, and neuromorphic computing as emerging paradigms. Identified challenges include training data scarcity, hardware-in-the-loop deployment, real-time computational constraints, and robustness against environmental perturbations. The paper concludes with a forward-looking perspective on future research directions aimed at bridging the gap between AI-driven control theory and practical MEMS device implementation.