A. Jurisson
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To reduce emissions and improve efficiency, modern aircraft designs are moving towards higher aspect ratios and lighter materials. While these design choices enhance performance, they also result in more flexible aircraft structures. This flexibility leads to greater interaction between rigid body motion and structural dynamics. Accurate modelling of these interactions is critical for evaluating aircraft handling qualities, predicting structural loads, tuning control laws for stability and performance, and developing simulators for pilot training.
System identification techniques provide a means to derive these models from flight test measurements. State-of-the-art system identification methods successfully capture the effects of structural dynamics. However, they rely on the assumption of (quasi-) steady aerodynamics. In steady aerodynamic models, changes in parameters such as angle of attack or control surface deflections are assumed to result in instantaneous changes in aerodynamic forces and moments. In reality, due to wake effects from unsteady aerodynamics, these forces and moments take time to develop, introducing delays in the response. Accurately capturing these delays is crucial for correctly predicting and modelling the aircraft’s dynamic behaviour. Failure to account for unsteady aerodynamics can lead to errors in load predictions, degraded handling quality assessments, and suboptimal control law design.
This dissertation develops a methodology for identifying a parametric flight dynamics and loads model from flight test measurements for a flexible aircraft that also include the effects of structural dynamics and unsteady aerodynamics. A two-step approach was adopted where the identification procedure consists of separate state estimation and parameter estimation steps. This allowed to perform model parameter estimation using a linear least squares approach. In contrast, alternative methods such as the output-error approach perform state estimation and model parameter estimation in a single nonlinear optimisation process. While this method can provide accurate results, it requires accurate initial parameter estimates to achieve convergence and a good fit, and it imposes a significantly higher computational load, making it less efficient for larger and more complex models.
A scaled Diana 2 glider unmanned aerial vehicle (UAV) was used as the flight test platform in this research. Using a UAV allowed to conduct flight testing with much fewer rules and regulations compared to full-scale aircraft testing, while also significantly lowering costs. A glider configuration was selected due to its high aspect ratio and flexible structure, making it well-suited for studying aeroelastic effects. Furthermore, the flight tests could be conducted at airspeeds and reduced frequencies corresponding to unsteady aerodynamic conditions... ...
System identification techniques provide a means to derive these models from flight test measurements. State-of-the-art system identification methods successfully capture the effects of structural dynamics. However, they rely on the assumption of (quasi-) steady aerodynamics. In steady aerodynamic models, changes in parameters such as angle of attack or control surface deflections are assumed to result in instantaneous changes in aerodynamic forces and moments. In reality, due to wake effects from unsteady aerodynamics, these forces and moments take time to develop, introducing delays in the response. Accurately capturing these delays is crucial for correctly predicting and modelling the aircraft’s dynamic behaviour. Failure to account for unsteady aerodynamics can lead to errors in load predictions, degraded handling quality assessments, and suboptimal control law design.
This dissertation develops a methodology for identifying a parametric flight dynamics and loads model from flight test measurements for a flexible aircraft that also include the effects of structural dynamics and unsteady aerodynamics. A two-step approach was adopted where the identification procedure consists of separate state estimation and parameter estimation steps. This allowed to perform model parameter estimation using a linear least squares approach. In contrast, alternative methods such as the output-error approach perform state estimation and model parameter estimation in a single nonlinear optimisation process. While this method can provide accurate results, it requires accurate initial parameter estimates to achieve convergence and a good fit, and it imposes a significantly higher computational load, making it less efficient for larger and more complex models.
A scaled Diana 2 glider unmanned aerial vehicle (UAV) was used as the flight test platform in this research. Using a UAV allowed to conduct flight testing with much fewer rules and regulations compared to full-scale aircraft testing, while also significantly lowering costs. A glider configuration was selected due to its high aspect ratio and flexible structure, making it well-suited for studying aeroelastic effects. Furthermore, the flight tests could be conducted at airspeeds and reduced frequencies corresponding to unsteady aerodynamic conditions... ...
To reduce emissions and improve efficiency, modern aircraft designs are moving towards higher aspect ratios and lighter materials. While these design choices enhance performance, they also result in more flexible aircraft structures. This flexibility leads to greater interaction between rigid body motion and structural dynamics. Accurate modelling of these interactions is critical for evaluating aircraft handling qualities, predicting structural loads, tuning control laws for stability and performance, and developing simulators for pilot training.
System identification techniques provide a means to derive these models from flight test measurements. State-of-the-art system identification methods successfully capture the effects of structural dynamics. However, they rely on the assumption of (quasi-) steady aerodynamics. In steady aerodynamic models, changes in parameters such as angle of attack or control surface deflections are assumed to result in instantaneous changes in aerodynamic forces and moments. In reality, due to wake effects from unsteady aerodynamics, these forces and moments take time to develop, introducing delays in the response. Accurately capturing these delays is crucial for correctly predicting and modelling the aircraft’s dynamic behaviour. Failure to account for unsteady aerodynamics can lead to errors in load predictions, degraded handling quality assessments, and suboptimal control law design.
This dissertation develops a methodology for identifying a parametric flight dynamics and loads model from flight test measurements for a flexible aircraft that also include the effects of structural dynamics and unsteady aerodynamics. A two-step approach was adopted where the identification procedure consists of separate state estimation and parameter estimation steps. This allowed to perform model parameter estimation using a linear least squares approach. In contrast, alternative methods such as the output-error approach perform state estimation and model parameter estimation in a single nonlinear optimisation process. While this method can provide accurate results, it requires accurate initial parameter estimates to achieve convergence and a good fit, and it imposes a significantly higher computational load, making it less efficient for larger and more complex models.
A scaled Diana 2 glider unmanned aerial vehicle (UAV) was used as the flight test platform in this research. Using a UAV allowed to conduct flight testing with much fewer rules and regulations compared to full-scale aircraft testing, while also significantly lowering costs. A glider configuration was selected due to its high aspect ratio and flexible structure, making it well-suited for studying aeroelastic effects. Furthermore, the flight tests could be conducted at airspeeds and reduced frequencies corresponding to unsteady aerodynamic conditions...
System identification techniques provide a means to derive these models from flight test measurements. State-of-the-art system identification methods successfully capture the effects of structural dynamics. However, they rely on the assumption of (quasi-) steady aerodynamics. In steady aerodynamic models, changes in parameters such as angle of attack or control surface deflections are assumed to result in instantaneous changes in aerodynamic forces and moments. In reality, due to wake effects from unsteady aerodynamics, these forces and moments take time to develop, introducing delays in the response. Accurately capturing these delays is crucial for correctly predicting and modelling the aircraft’s dynamic behaviour. Failure to account for unsteady aerodynamics can lead to errors in load predictions, degraded handling quality assessments, and suboptimal control law design.
This dissertation develops a methodology for identifying a parametric flight dynamics and loads model from flight test measurements for a flexible aircraft that also include the effects of structural dynamics and unsteady aerodynamics. A two-step approach was adopted where the identification procedure consists of separate state estimation and parameter estimation steps. This allowed to perform model parameter estimation using a linear least squares approach. In contrast, alternative methods such as the output-error approach perform state estimation and model parameter estimation in a single nonlinear optimisation process. While this method can provide accurate results, it requires accurate initial parameter estimates to achieve convergence and a good fit, and it imposes a significantly higher computational load, making it less efficient for larger and more complex models.
A scaled Diana 2 glider unmanned aerial vehicle (UAV) was used as the flight test platform in this research. Using a UAV allowed to conduct flight testing with much fewer rules and regulations compared to full-scale aircraft testing, while also significantly lowering costs. A glider configuration was selected due to its high aspect ratio and flexible structure, making it well-suited for studying aeroelastic effects. Furthermore, the flight tests could be conducted at airspeeds and reduced frequencies corresponding to unsteady aerodynamic conditions...
Ultraefficient, high-aspect-ratio wings offer a promising solution for reducing emissions in next-generation aircraft. However, these designs are sensitive to atmospheric disturbances and prone to instability. While active control strategies can mitigate structural loads and stabilize the system, their development is challenging due to the uncertain and time-varying nature of aeroelastic systems. This article addresses these challenges with a direct, adaptive, data-driven approach. The proposed data-enabled policy optimization algorithm leverages sample covariance to directly learn and adapt control strategies from a single batch of persistently exciting, closed-loop input–output data. A forgetting factor mechanism enhances adaptability to time-varying dynamics during operation. The algorithm is explicit and recursive, requiring only a single step of projected gradient descent per sample, improving computational efficiency and enabling real-time application. Numerical simulations demonstrate that the proposed algorithm effectively suppresses unstable flutter, alleviates structural loads, adapts to dynamic time variations, and minimizes control effort—all without requiring prior knowledge of system dynamics or disturbances.
...
Ultraefficient, high-aspect-ratio wings offer a promising solution for reducing emissions in next-generation aircraft. However, these designs are sensitive to atmospheric disturbances and prone to instability. While active control strategies can mitigate structural loads and stabilize the system, their development is challenging due to the uncertain and time-varying nature of aeroelastic systems. This article addresses these challenges with a direct, adaptive, data-driven approach. The proposed data-enabled policy optimization algorithm leverages sample covariance to directly learn and adapt control strategies from a single batch of persistently exciting, closed-loop input–output data. A forgetting factor mechanism enhances adaptability to time-varying dynamics during operation. The algorithm is explicit and recursive, requiring only a single step of projected gradient descent per sample, improving computational efficiency and enabling real-time application. Numerical simulations demonstrate that the proposed algorithm effectively suppresses unstable flutter, alleviates structural loads, adapts to dynamic time variations, and minimizes control effort—all without requiring prior knowledge of system dynamics or disturbances.
This paper presents a method for identifying flight dynamics models for aircraft that includes effects from the flexible structure and the effects from unsteady aerodynamics. In the time domain, the unsteady aerodynamic effects are often modelled using aerodynamic lag states. The proposed method involves first determining the poles that govern the dynamics for these lag states from flight data. This is followed by reconstructing the time signal histories for these lag states so that they can then be used as part of the model fitting procedure. Flight tests were conducted using a scaled Diana2 glider unmanned aerial vehicle (UAV) in order to collect experimental data for modelling. To be able to measure the response of the aircraft and its structure, the glider was instrumented with a wide range of sensors including accelerometers, gyroscopes and strain gauges placed across the aircraft structure. During the flight, various excitation manoeuvres were conducted by the pilot while the aircraft responses were collected. From these measurements, a full flight dynamics model consisting of both lateral and longitudinal dynamics was then identified. Additionally, a model predicting the tail and wing root loads was also identified. First, a rigid aircraft model was fitted that was then extended with states corresponding to the flexible modes and aerodynamic lags. Comparison between the rigid and extended model showed that the addition of structural modes and aerodynamic lag states to the identified models can lead up to 30% improvement in predicting aircraft responses. In conclusion, the method developed and presented in this paper is able to identify flight dynamics models from flight data that more accurately capture the dynamics of flexible aircraft by including effects from the flexible structure and unsteady aerodynamics.
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This paper presents a method for identifying flight dynamics models for aircraft that includes effects from the flexible structure and the effects from unsteady aerodynamics. In the time domain, the unsteady aerodynamic effects are often modelled using aerodynamic lag states. The proposed method involves first determining the poles that govern the dynamics for these lag states from flight data. This is followed by reconstructing the time signal histories for these lag states so that they can then be used as part of the model fitting procedure. Flight tests were conducted using a scaled Diana2 glider unmanned aerial vehicle (UAV) in order to collect experimental data for modelling. To be able to measure the response of the aircraft and its structure, the glider was instrumented with a wide range of sensors including accelerometers, gyroscopes and strain gauges placed across the aircraft structure. During the flight, various excitation manoeuvres were conducted by the pilot while the aircraft responses were collected. From these measurements, a full flight dynamics model consisting of both lateral and longitudinal dynamics was then identified. Additionally, a model predicting the tail and wing root loads was also identified. First, a rigid aircraft model was fitted that was then extended with states corresponding to the flexible modes and aerodynamic lags. Comparison between the rigid and extended model showed that the addition of structural modes and aerodynamic lag states to the identified models can lead up to 30% improvement in predicting aircraft responses. In conclusion, the method developed and presented in this paper is able to identify flight dynamics models from flight data that more accurately capture the dynamics of flexible aircraft by including effects from the flexible structure and unsteady aerodynamics.
This paper proposes an extension to the traditional flight path reconstruction filter to simultaneously reconstruct the aircraft rigid body states together with modal amplitudes and velocities of the structure. To achieve this, the filter makes use of additional accelerometers, gyroscopes and strain gauges placed across the aircraft structure. These measurements are used in a Kalman filter together with kinematic equations of a flexible aircraft and structural mode shapes. First a simulation model is used to evaluate the filter performance on reference signals where the true state is known. Finally, flight test measurements obtained using an instrumented Diana 2 scaled glider are used to validate the filter.
...
This paper proposes an extension to the traditional flight path reconstruction filter to simultaneously reconstruct the aircraft rigid body states together with modal amplitudes and velocities of the structure. To achieve this, the filter makes use of additional accelerometers, gyroscopes and strain gauges placed across the aircraft structure. These measurements are used in a Kalman filter together with kinematic equations of a flexible aircraft and structural mode shapes. First a simulation model is used to evaluate the filter performance on reference signals where the true state is known. Finally, flight test measurements obtained using an instrumented Diana 2 scaled glider are used to validate the filter.
Conference paper
(2022)
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Andres Jürisson, Huub Timmermans, Bart Eussen, Coen de Visser, Roeland de Breuker
A ground vibration test was conducted with a 1:3 scaled Diana 2 glider model where the modal parameters were estimated using the accelerometers, gyroscopes and strain gauges integrated in the test aircraft and validated using externally attached calibrated accelerometers and commercial software. These modal parameters were then used to update a FEM model of the glider together with static load tests and component mass measurements. The goal for this updated and fitted FEM model is then to build an aeroelastic model for flexible aircraft flight dynamics simulator.
...
A ground vibration test was conducted with a 1:3 scaled Diana 2 glider model where the modal parameters were estimated using the accelerometers, gyroscopes and strain gauges integrated in the test aircraft and validated using externally attached calibrated accelerometers and commercial software. These modal parameters were then used to update a FEM model of the glider together with static load tests and component mass measurements. The goal for this updated and fitted FEM model is then to build an aeroelastic model for flexible aircraft flight dynamics simulator.
Netherlands Aerospace Centre (NLR) and Delft University of Technology (TUD) have obtained an unmanned aerial vehicle to serve as a test platform for future aeroservoelastic flight testing. The purpose of this testbed is to collect data about flexible aircraft flight dynamic responses and loads on the aircraft at unsteady airflow conditions for flexible aircraft system identification. An overview of the 1:3 scaled Diana 2 glider is presented together with numerous tests that were conducted to characterise the test platform. Moment of inertia of the aircraft was determined using a pendulum and rotational swing setup, control surface dynamics were identified from sine sweeps and the structural modes and frequencies were obtained from a ground vibration test. Finally, a low cost and modular data acquisition system was built to collect all the sensor measurements. This data acquisition system is presented together with an overview of its performance.
...
Netherlands Aerospace Centre (NLR) and Delft University of Technology (TUD) have obtained an unmanned aerial vehicle to serve as a test platform for future aeroservoelastic flight testing. The purpose of this testbed is to collect data about flexible aircraft flight dynamic responses and loads on the aircraft at unsteady airflow conditions for flexible aircraft system identification. An overview of the 1:3 scaled Diana 2 glider is presented together with numerous tests that were conducted to characterise the test platform. Moment of inertia of the aircraft was determined using a pendulum and rotational swing setup, control surface dynamics were identified from sine sweeps and the structural modes and frequencies were obtained from a ground vibration test. Finally, a low cost and modular data acquisition system was built to collect all the sensor measurements. This data acquisition system is presented together with an overview of its performance.