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

Abstract

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 pressure. 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 advancement also in the aeronautical braking context and, secondly, whether the wheel slip can be effectively 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 performance 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.