Yaw control in fixed wing UAVs

This research provides a comprehensive review of yaw control and stability in fixed-wing Unmanned Aerial Vehicles (UAVs), a domain that has historically received less attention than pitch and roll control. While UAVs have revolutionized both military and civilian aviation through their adaptability and cost efficiency, maintaining directional stability — especially around the vertical (yaw) axis — remains a critical technical challenge. The study explores the aerodynamic principles governing yaw motion, identifies the limitations of conventional stabilization methods, and assesses modern control systems that aim to improve flight performance under varying conditions such as turbulence, crosswinds, and low-speed maneuvers.

The paper systematically analyzes multiple control strategies for managing yaw — notably Proportional-Integral-Derivative (PID), Linear Quadratic Regulator (LQR), and Fuzzy Logic Controllers (FLC). The LQR and FLC models are compared through simulation-based experiments, highlighting trade-offs between mathematical precision and adaptive flexibility. While LQR demonstrates faster response and higher accuracy, FLC offers smoother and more stable performance without overshoot, making it advantageous for applications where comfort and gradual correction are key. The study also reviews PID’s enduring relevance due to its simplicity, low computational demand, and robust performance in real-time embedded systems, despite its tuning limitations for highly nonlinear flight dynamics.

Ultimately, the paper argues for a hybridized approach that merges the optimization efficiency of LQR with the adaptive intelligence of fuzzy logic systems to achieve superior yaw control. Through its detailed review of aerodynamic behavior, mathematical modeling, and control algorithms, this work bridges theoretical understanding and practical application, underscoring how improved yaw stabilization directly contributes to UAV reliability, autonomy, and precision across diverse mission profiles — from surveillance to environmental monitoring.