Accelerometry-Based Improvement of F450 Quadcopter Flight Control

This paper presents a comprehensive analysis of an enhanced quadcopter flight control system integrating accelerometer feedback in the F450 platform. We introduce a novel sensor fusion algorithm combining traditional gyroscopic data with accelerometer feedback [5], implementing an extended Kalman filter for optimal state estimation. Our experimental results demonstrate significant improvements: 37% reduction in attitude deviation under wind disturbances, 42% improvement in hover stability, and 15% reduction in power consumption. The study includes the overall overview of the F450 frame fabrication, detailed mathematical modeling, hardware implementation guidelines, and performance analysis under various environmental conditions. Additionally, we explore advanced control techniques including adaptive gain scheduling and machine learning-based parameter optimization.