Design optimisation and experimental verification of a UAV’s landing gear buffer

Abstract

The landing gear is a critical aircraft system that absorbs the kinetic energy of the vertical load and reduces the aircraft’s impact at touchdown. This paper proposes a hybrid optimisation method for further optimising the buffer performance of a mature UAV’s main landing gear. This method combines a propagation (BP) neural network method with a genetic algorithm (GA). A landing gear dynamics simulation model is established based on the software LMS Virtual.Lab Motion, and validate the model with tests. The optimisation model is established using a BP neural network and solved using a genetic algorithm. The maximum vertical load, the efficiency coefficient of buffer, and the efficiency coefficient of buffer system are used as the optimisation targets. The air chamber initial gas pressure, initial volume of buffer, and diameter of main oil hole are selected as the optimisation parameters, and the values after optimisation are 148 MPa, 165.2 mL, and 3.17 mm, respectively. The results show that the efficiency coefficient of buffer is increased by 3.7% and the efficiency coefficient of buffer system is increased by 1.13% and the maximum vertical load decreased by 0.6%, which indicated that the hybrid optimisation method is effective for landing gear buffering performance.

Keywords:

• Landing gear
• dynamic model
• artificial neural network
• buffer performance
• parameter optimisation
• damping

Acknowledgements

We would like to thank Editage (www.editage.com) for English language editing.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors received no financial support for the research, authorship, or publication of this article.