A wheel slip control scheme for aeronautical braking applications based on neural network estimation

Due to safety reasons, anti-skid braking control strategies in aircraft can rely only on sensors that are integral to the landing gear, and these currently are limited to wheel rotational speed and braking pres-sure. Thus, up to now anti-lock-braking systems (ABS) are developed as threshold-based algorithms that regulate the wheel deceleration. However, as it is known from the Automotive field, slip control offers superior closed-loop properties and robustness, paired with a much easier and time-saving tuning phase. This work aims at investigating first of all if slip control does indeed offer the same performance ad-vancement also in the aeronautical braking context and, secondly, whether the wheel slip can be effec-tively estimated and employed in a closed-loop braking controller without the need of additional sensors on the landing gear. To do this, we propose a wheel slip control scheme where a data-driven approach using a neural network architecture is used to solve the problem of wheel slip estimation. The proposed control scheme is tested within a very realistic simulation setting, capturing all the non-linear effects peculiar of the aeronautical context, and compared with a standard deceleration-based ABS in multiple braking maneuvers carried out within a large operational envelope. The results show that superior per-formance and robustness can indeed be achieved with the proposed control approach, paving the way for the adoption of wheel slip abs strategies also for aircraft braking. (c) 2022 European Control Association. Published by Elsevier Ltd. All rights reserved.

Automated summary

This article investigates whether slip control can provide the same performance improvement in the aeronautical braking context as it does in the automotive field, and whether wheel slip can be effectively estimated and employed in a closed-loop braking controller without the need for additional sensors on the landing gear. A data-driven approach using a neural network architecture is proposed to solve the problem of wheel slip estimation. The results demonstrate that the proposed control approach can achieve superior performance and robustness, paving the way for the adoption of wheel slip ABS strategies in aircraft braking.