Adaptive MEMS Gyroscope Regulation Using Steerable Graph Learning Architectures

Abstract

Micro-Electro-Mechanical Systems (MEMS) gyroscopes are extensively utilized in aerospace navigation, autonomous vehicles, robotics, industrial automation, consumer electronics, and Internet of Things (IoT) systems due to their compact size, low power consumption, and cost-effective implementation. Despite their widespread adoption, MEMS gyroscopes frequently suffer from performance degradation caused by bias drift, scale factor instability, thermal variations, vibration disturbances, nonlinear dynamics, and environmental uncertainties. These challenges reduce measurement accuracy and limit the reliability of gyroscope-based navigation and motion sensing applications. Conventional calibration and regulation techniques often rely on static mathematical models that are unable to adapt efficiently to changing operational conditions. Recent advances in graph-based machine learning and adaptive neural intelligence provide promising opportunities for intelligent sensor regulation and dynamic error compensation. This research proposes an Adaptive MEMS Gyroscope Regulation Using Steerable Graph Learning Architectures (AMGR-SGLA) framework that integrates adaptive signal preprocessing, graph-based feature representation, steerable graph neural learning, intelligent regulation mechanisms, dynamic drift compensation, and real-time performance optimization.