Bandwidth-Constrained Intelligent Control Framework for MEMS Motion Stabilization

Abstract

Micro-Electro-Mechanical Systems (MEMS) are widely employed in aerospace, automotive, biomedical, industrial automation, navigation, and communication applications due to their compact size, high sensitivity, low power consumption, and rapid response characteristics. However, maintaining stable MEMS motion under bandwidth-constrained communication and control environments remains a significant challenge. Limited communication bandwidth, transmission delays, signal losses, sensor noise, and dynamic environmental disturbances can adversely affect motion stability, control accuracy, and system reliability. Conventional control approaches often require continuous high-rate feedback transmission, resulting in excessive communication overhead and reduced efficiency in resource-constrained environments. Therefore, intelligent control mechanisms capable of achieving robust motion stabilization while operating under limited bandwidth conditions have become increasingly important. This research proposes a Bandwidth-Constrained Intelligent Control Framework for MEMS Motion Stabilization (BCICF-MMS). The framework integrates intelligent feedback scheduling, adaptive bandwidth allocation, deep learning-based state estimation, predictive control mechanisms, and dynamic stabilization strategies to ensure accurate MEMS motion control under communication constraints. The proposed architecture utilizes neural state prediction models to compensate for delayed or missing feedback information while optimizing control decisions using adaptive bandwidth-aware policies. Multi-domain motion features, including displacement, velocity, acceleration, and vibration characteristics, are fused to improve stabilization performance and disturbance rejection capability.