# Improved design of an active landing gear for a passenger aircraft using multi-objective optimization technique

arXiv: https://arxiv.org/abs/2506.22870 · HTML: https://mlsanity.com/paper/2506.22870/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2025.

## Built on by (most cited first, 1 of 1)

- [A Deep Learning Framework for Real-Time Image Processing in Medical Diagnostics: Enhancing Accuracy and Speed in Clinical Applications](https://mlsanity.com/paper/2510.16611/) (2025)

## Abstract (arXiv)

The landing gear system is a major aircraft subsystem that must withstand extreme forces during ground maneuvers and absorb vibrations. While traditional systems perform well under normal conditions, their efficiency drops under varying landing and runway scenarios. This study addresses this issue by simultaneously optimizing controller coefficients, parameters of a nonlinear hydraulic actuator integrated into the traditional shock absorber, and a vibration absorber using a bee-inspired multi-objective algorithm. To demonstrate adaptability, the paper includes sensitivity analysis for three-point landings affected by added payload and touchdown speed, and robustness analysis for one- and two-point landings under emergency wind conditions. The dynamic flight equations of an Airbus A320-200 during landing are derived and solved numerically. Results show that the active shock absorber system, optimized via two bee-based algorithms, outperforms the passive system in reducing bounce and pitch displacements and momenta, suspension travel, and impact force in both time and frequency domains. This leads to significantly improved passenger comfort and potentially longer structural fatigue life, demonstrating industrial applicability.