Research on helicopter electric tail rotor control method based on fuzzy neural network

The stability of the helicopter mandates the tail rotor’s high efficiency and reliability, and precise control over the distributed electric tail rotor is pivotal in enhancing yaw attitude control efficiency and maneuverability. This study presents a fuzzy neural network control methodology incorporating feed-forward compensation, aimed at achieving meticulous control over the distributed electric tail rotor system. Initially, a comprehensive analysis of the helicopter’s motion characteristics was conducted through six-degree-of-freedom physical modeling. Subsequently, a hybrid approach combining fuzzy control with neural networks was devised to optimize the balance between stability and dynamic response in the distributed tail rotor system. This method thoroughly accounts for the nonlinear control characteristics, integrating a feed-forward compensation mechanism to facilitate rapid responses to external perturbations through preemptive prediction of posture changes in the distributed electric tail rotor system. In the final phase, the entire distributed electric tail rotor control system model was constructed in MATLAB/Simulink, where the control strategy of dynamically adjusting the PI parameters through a fuzzy neural network algorithm underwent functional simulation. The findings demonstrate that, relative to PID control and fuzzy PID control, the neural network control method facilitates precise yaw angle control under varying flight condition