Mr. Tigran Mkhoyan
Mr. Tigran Mkhoyan
The Netherlands
Aerospace Structures and Materials Department, Delft University of Technology
PhD Candidate at the Faculty of Aerospace Engineering
Mr. Tigran Mkhoyan is a PhD candidate at Faculty of Aerospace Engineering of the Delft University of Technology, Department of Aerospace Structures and Materials, currently working on the Smart-X project, an Autonomous Smart Morphing Wing.

Mr. Mkhoyan graduated from the Delft University of Technology, department Control and Operations, with the specialisation in advanced control and motion cueing for Dynamic Flight Simulation. Determined to bridge the gap between aeroelasticity and advanced control and push the advancements in aircraft technology towards a new generation of Smart aircraft. Currently also working on his own startup X::HUD (Smart motorcycle helmet).

Topic & Abstract

Adaptive state estimation and Real-Time tracking for control of a flexible wing with machine learning and AI

Advancements in intelligent aircraft controller design, paired with increasingly flexible and efficient aircrafts concepts, creates the need for the development of novel (smart) adaptive sensing suitable for aeroelastic state estimation. In contrast to rigid states, aeroelastic state estimation requires more measurement points (displacements and forces) across the span to capture the vibrational shapes of the wing undergoing excitations. A potentially universal and non-invasive approach is visual tracking. In this study, a method was developed and tested with the purpose of reducing the effort and cost associated with state estimation of flexible aeroelastic structures. Do so, visual information is used to estimate the elastic states real-time such that these can be provided as feedback to the controller for in-flight performance optimization. The method combines KCF (Kernelized Correlation Filter), which is a purely visual filter, with an augmented Kalman Filter to learn the parameters of the system on-line and account for the dynamics of the motion. Results from wind tunnel-test and flight-test are discussed using an embedded computing system. Further investigations are made on how to learn to predict displacements with deep learning using a convolutional neural network and laser vibrometer data as ground truth.