Abstract
Heterogeneity of the small aircraft category (e.g., small air transport (SAT), urban air mobility (UAM), unmanned aircraft system (UAS)), modern avionic solution (e.g., fly-by-wire (FBW)) and reduced aircraft (A/C) size require more compact, integrated, digital and modular air data system (ADS) able to measure data from the external environment. The MIDAS project, funded in the frame of the Clean Sky 2 program, aims to satisfy those recent requirements with an ADS certified for commercial applications. The main pillar lays on a smart fusion between COTS solutions and analytical sensors (patented technology) for the identification of the aerodynamic angles. The identification involves both flight dynamic relationships and data-driven state observer(s) based on neural techniques, which are deterministic once the training is completed. As this project will bring analytical sensors on board of civil aircraft as part of a redundant system for the very first time, design activities documented in this work have a particular focus on airworthiness certification aspects. At this maturity level, simulated data are used, real flight test data will be used in the next stages. Data collection is described both for the training and test aspects. Training maneuvers are defined aiming to excite all dynamic modes, whereas test maneuvers are collected aiming to validate results independently from the training set and all autopilot configurations. Results demonstrate that an alternate solution is possible enabling significant savings in terms of computational effort and lines of codes but they show, at the same time, that a better training strategy may be beneficial to cope with the new neural network architecture.
Highlights
Air data systems (ADSs) are adopted on air vehicles to measure a set of data from the external environment
Pressure probes and static ports are pneumatically connected to a central air data computer whereas the flow angle vanes and the TAT sensor are usually electronically connected to the ADC
Even though the presence of autopilot modes and control laws, as well known, affect the A/C dynamic behavior, they do not influence generic state observers if they are fed with current output and control surfaces, as showed in Another important aspect is related to real operating scenario: a common virtual sensor able to estimate at the same time both angle of attack (AoA) and angle of sideslip (AoS) could be beneficial in terms of required computational time and for ceritification aspects as mentioned before
Summary
Air data systems (ADSs) are adopted on air vehicles to measure a set of data from the external environment. The advent of distributed avionics, e.g., ARINC 664 networks on Airbus A380, A400M, A350 and Boeing 787, has been seen as a significant booster for a better exploitation of onboard data to be used, for instance, by other subsystems for redundancy purposes Within this scenario, innovative ADS for FBW applications as part of a redundant ADS is introduced with the MIDAS project funded in the SAT category of Clean Sky 2 programme [12]. This solution, basically a state observer obtained with a data-driven methodology, is able to estimate AoA and AoS with analytical sensors [15,16,17] exploiting. This work deals with preliminary design activities accomplished to define analytical sensors (or virtual ones) for AoA and AoS estimation exploiting simulated flight data.
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