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Open AccessArticle

A Data-Driven Approach to Identify Flight Test Data Suitable to Design Angle of Attack Synthetic Sensor for Flight Control Systems

Department of Mechanical and Aerospace Engineering DIMEAS, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy
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This paper is an extended version of our paper published in Lerro, A.; Brandl, A.; Battipede, M.; Gili, P. Air Data Virtual Sensor: A Data-Driven Approach to Identify Flight Test Data Suitable for the Learning Process. In Proceedings of the 5th CEAS Conference on Guidance, Navigation and Control (EuroGNC2019), Milano, Italy, 3–5 April 2019.
Aerospace 2020, 7(5), 63; https://doi.org/10.3390/aerospace7050063
Received: 23 April 2020 / Accepted: 19 May 2020 / Published: 23 May 2020
(This article belongs to the Special Issue Control and Optimization Problems in Aerospace Engineering)
Digital avionic solutions enable advanced flight control systems to be available also on smaller aircraft. One of the safety-critical segments is the air data system. Innovative architectures allow the use of synthetic sensors that can introduce significant technological and safety advances. The application to aerodynamic angles seems the most promising towards certified applications. In this area, the best procedures concerning the design of synthetic sensors are still an open question within the field. An example is given by the MIDAS project funded in the frame of Clean Sky 2. This paper proposes two data-driven methods that allow to improve performance over the entire flight envelope with particular attention to steady state flight conditions. The training set obtained is considerably undersized with consequent reduction of computational costs. These methods are validated with a real case and they will be used as part of the MIDAS life cycle. The first method, called Data-Driven Identification and Generation of Quasi-Steady States (DIGS), is based on the (i) identification of the lift curve of the aircraft; (ii) augmentation of the training set with artificial flight data points. DIGS’s main aim is to reduce the issue of unbalanced training set. The second method, called Similar Flight Test Data Pruning (SFDP), deals with data reduction based on the isolation of quasi-unique points. Results give an evidence of the validity of the methods for the MIDAS project that can be easily adopted for generic synthetic sensor design for flight control system applications. View Full-Text
Keywords: flight control system; air data system; flight dynamics; synthetic sensor; virtual sensor; analytical redundancy; avionics; neural network; angle of attack; aerodynamic angle flight control system; air data system; flight dynamics; synthetic sensor; virtual sensor; analytical redundancy; avionics; neural network; angle of attack; aerodynamic angle
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Lerro, A.; Brandl, A.; Battipede, M.; Gili, P. A Data-Driven Approach to Identify Flight Test Data Suitable to Design Angle of Attack Synthetic Sensor for Flight Control Systems. Aerospace 2020, 7, 63.

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