Aircraft Trim Condition Detection Using Flight Test Data and Interval Analysis

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Authors: Mouhamadou Wade, Pierre-Olivier Tardif, Vincent Myrand-Lapierre, Georges Ghazi, Ruxandra Botez
Cited by: 1
References: 12

Abstract

This paper presents the results of a study conducted at the Laboratory of Applied Research in Active Controls, Avionics, and AeroServoElasticity (LARCASE). The research focuses on identifying the trim conditions of an aircraft using flight test data and an interval analysis method. In the field of flight simulator design, both the quantity and quality of data are crucial. Indeed, accurate and reliable data is essential for calibrating simulators, as it directly affects their fidelity and performance. To gather this data, pilots perform predefined maneuvers during flight tests. Traditionally, domain experts manually analyze the collected data to assess its quality. However, this manual approach is time-consuming, relies heavily on highly skilled personnel, and often lacks reproducibility. The primary objective of this study is to enable users to automatically identify the trim intervals of an aircraft without requiring knowledge of its geometric parameters, stability, or control derivatives. The data used, provided by CAE, includes pilot commands and inertial measurements from an unidentified business jet. The methodology involves analyzing variations in control surfaces (e.g., ailerons, elevators, rudder) and flight parameters (e.g., angles, rates) to determine trim intervals. Tolerances are defined, and a sliding window scans the signals to identify intervals where these tolerances are satisfied. Once these intervals are identified, an intersection of the results is performed. The intervals in which all tolerances are respected are classified as trims. This approach was applied to flight maneuvers that included steady wings-level flight, steady turning flight, steady pull-up or pushover, and steady roll. The results demonstrate that the developed tool effectively segments flight test data into trim and maneuvering phases. By automating this process, the methodology provides a more efficient and reproducible alternative to traditional manual methods while achieving accurate results without relying on the aircraft’s weight or geometric characteristics.

Fields of Study & Subjects

Aerospace and Aviation Technology Aeroelasticity and Vibration Control Model Reduction and Neural Networks

Publication details

Journal / source
AIAA SCITECH 2026 Forum
Publisher
American Institute of Aeronautics and Astronautics
Publication date
8 January 2026