VIBRATION CONTROL OF LANDING GEAR IN VERTICAL TAKE-OFF AND LANDING AIRCRAFT USING ARTFICIAL NEURAL NETWORK BASED APPROACHES

Durmuşoğlu A.

BILTEK-XII 12th INTERNATIONAL BILTEK CONGRESS ON CURRENT DEVELOPMENTS IN SCIENCE, TECHNOLOGY AND SOCIAL SCIENCES , Ankara, Türkiye, 23 - 24 Eylül 2025, (Yayınlanmadı)

  • Yayın Türü: Bildiri / Yayınlanmadı
  • Basıldığı Şehir: Ankara
  • Basıldığı Ülke: Türkiye
  • Hakkari Üniversitesi Adresli: Evet

Özet

Vertical Takeoff and Landing (VTOL) aircraft have gained strategic importance in both military and civilian sectors in recent years due to their ability to operate without the need for a runway. Their flexibility, especially in limited spaces such as urban transportation, search and rescue operations, marine platforms, and aircraft carriers, makes these vehicles indispensable for the future of air transport. However, one of the most significant challenges for VTOL aircraft is the high-amplitude vibrations that occur in the landing gear during the vertical landing and takeoff phases. These vibrations accelerate structural fatigue due to impact loads transferred to the fuselage, shorten the lifespan of landing gear components, and seriously reduce passenger comfort. During landing, ground roughness, aerodynamic effects caused by rotor or jet currents, the ground effect phenomenon, and sudden load changes significantly complicate the dynamics of the landing gear.

In this study, an artificial neural network (ANN)-based control approach has been developed to suppress vibrations occurring in the landing gear of VTOL aircraft. The landing gear was modeled as a mass-spring-damper system, and random inputs from the ground were included in the model. The ANN-based controller aims to minimize vertical accelerations transmitted to the body, keep stroke usage within safe limits, and reduce impact loads generated during landing. A comprehensive performance analysis was conducted by comparing the proposed method with classical PID control and semi-active damping strategies.

The simulation results obtained show that the ANN-based approach exhibits superior performance, particularly in reducing peak acceleration, impact forces, and structural loads. This result contributes to VTOL aircraft achieving higher standards in both safety and comfort. In conclusion, artificial neural network-based control methods offer a powerful and viable solution for landing gear vibration control in future eVTOL concepts and urban air mobility applications.

Keywords: Vertical Take-Off and Landing (VTOL), Landing Gear Dynamics, Artificial Neural Networks, Vibration Control