Development and testing of a complementary sensor network for robust estimation of maneuver and gust loads
Deutscher Luft- und Raumfahrtkongress (2023)
Contribution to Conference
Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V.
In this publication, a complementary sensor network is presented with the primary objective of increasing the robustness of structural loads estimation. This augmentation is achieved through the combination of measurement methodologies and model-based load observers, thereby creating synergistic effects that mitigate the limitations associated with each approach. This work outlines the development of the complementary sensor network by means of laboratory tests and virtual flight tests. The sensor technologies employed include strain gauges, fiber bragg sensors, inertial measurement units, camera-based optical deformation measurements, and MEMS pressure measurement profiles. For each of these technologies, the laboratory test setup and testing process, alongside the derivation of sensor models for virtual testing of the sensor network is presented. The redundant and partially complementary sensors are fused through the utilization of both local and centralized fusion using Kalman filters and machine learning based approaches. The local fusion strategy exploits the integral correlation between inertial measurement unit (IMU) and camera data at specific observation points, establishing the basis for employing a data-driven local-model network approach wherein local deformations are trained on structural loads data. The centralized loads fusion combines a data association algorithm based on a quadruple-voting scheme and an extended Kalman filter. Finally, the performance and robustness of the whole sensor network is demonstrated based on virtual flight tests considering a load sensor failure.
Sensornetwork and observer network
virtual flight tests
380: Commerce, Communications, Transport
690: Building, Construction