Virtual sensing of wheel position in ground-steering systems for aircraft using digital twins

Abstract

The ground-steering system is a part of the nose landing gear, which is fundamental to an aircraft’s safety. A sensing mechanism estimates the wheel direction, which is then fed back to the controller in order to calculate the error between the desired steering angle and the actual steering angle. As in many safety-critical control systems, the sensing mechanism for the nose wheel direction requires the use of multiple redundant sensors to estimate the same controlled signal. A virtual sensing technique is commonly employed, which estimates the steering angle using the measurements of multiple remote displacement sensors. The wheel position is then calculated on the basis of the nonlinear alignment of these sensors.
In practice, however, each sensor is subject to uncertainty, minor and major faults and there is also ambiguity associated with the estimate of the steering angle because of the geometric nonlinearity. The redundant sensor outputs are thus different from each other, and it is important to reliably estimate the controlled signal under these conditions.
This paper presents the development of a digital twin of the ground-steering system, in which the effect of uncertainties and faults can be systematically analysed. A number of state estimation algorithms are investigated under several scenarios of uncertainty and sensor faults. Two of these algorithms are based on a least squares estimation approach, the other algorithm, instead, calculates the steering angle estimate using a soft-computing approach. It is shown that the soft-computing estimation algorithm is more robust than the least squares based methods in the presence of uncertainties and sensor faults. The propagation of an uncertainty interval from the sensor outputs to the steering angle estimate is also investigated, in order to calculate the error bounds on the estimated controlled signal. The optimal arrangement of the sensors is also investigated using a parametric study of the uncertainty propagation, in which the optimal model parameters are the ones that generates the smallest uncertainty interval for the estimate.

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Identifiers

ORCID for Mattia Dal Borgo: orcid.org/0000-0003-4263-0513

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