Aerodynamic Model Identification Research Articles

Stalls and Splines: Current Trends in Flight Testing and Aerodynamic Model Identification

Aerodynamic model identification remains essential for simulator operations and control system design and operations. In this paper, state-of-the-art methodologies for aerodynamic model identification and validation are presented, together with a number of novel applications of the identified models that were recently investigated and developed at the Faculty of Aerospace Engineering of Delft University of Technology. In particular, this paper focuses on methodologies for identifying models of aerodynamic stall from flight data, as well as multivariate spline-based aerodynamic model identification methods, together with their applications in flight simulation and advanced flight control.

Longitudinal Parameter Estimation Using Wind Tunnel and Simulated Flight Data of Missile Configuration

The present study makes an attempt to estimate aerodynamic parameters from a typical flight data of a short range missile. Exhaustive wind tunnel testing was conducted to generate longitudinal force and moment coefficients at low speed. Identification methods were applied on the selected wind tunnel data to capture the general form of the aerodynamic model. During the application of Maximum likelihood (ML) method, the estimation algorithm assumed this wind tunnel identified aerodynamic model to be exact. To avoid any requirement of postulation of aerodynamic model, the Delta method was applied to estimate the aerodynamic parameters. The Delta method used measured aircraft motion and control variables as the inputs to the Feed Forward Neural Network and the aerodynamic force or moment coefficient was the output for training the Feed Forward Neural Network. The application of the Delta method results in large scatters in the estimated parameters. To overcome this problem of large scatter, the Delta method was modified by changing the training strategy. The Delta method with new training strategy will be referred as the Modified Delta method. It is expected that the proposed Modified Delta method would result in estimates with less uncertainties. Further to check the robustness of the ML, Delta and the Modified delta methods, the estimation was also carried out with flight data having known measurement noise. The effect of control input form in the accuracy of estimates obtained by ML, the Delta and the Modified Delta methods are also studied. It is observed that the Modified Delta method can advantageously be applied on the flight data of a tactical missile to estimate aerodynamic parameters. The paper progresses with the description of the generation of wind tunnel data and aerodynamic model identification using selected wind tunnel data. Finally it concludes by demonstrating applicability of ML, the Delta and the Modified Delta methods on simulated flight data of a typical short range tactical missile configuration.

Development of an aeroelastic model based on system identification using boundary elements method

PurposeThe purpose of this paper is to analyze the stability of aeroelastic systems using a novel reduced order aeroelastic model.Design/methodology/approachThe proposed aeroelastic model is a reduced-order model constructed based on the aerodynamic model identification using the generalized aerodynamic force response and the unsteady boundary element method in various excitation frequency values. Due to the low computational cost and acceptable accuracy of the boundary element method, this method is selected to determine the unsteady time response of the aerodynamic model. Regarding the structural model, the elastic mode shapes of the shell are used.FindingsThree case studies are investigated by the proposed model. In the first place, a typical two-dimensional section is introduced as a means of verification by approximating the Theodorsen function. As the second test case, the flutter speed of Advisory Group for Aerospace Research and Development 445.6 wing with 45° sweep angle is determined and compared with the experimental test results in the literature. Finally, a complete aircraft is considered to demonstrate the capability of the proposed model in handling complex configurations.Originality/valueThe paper introduces an algorithm to construct an aeroelastic model applicable to any unsteady aerodynamic model including experimental models and modal structural models in the implicit and reduced order form. In other words, the main advantage of the proposed method, further to its simplicity and low computational effort, which can be used as a means of real-time aeroelastic simulation, is its ability to provide aerodynamic and structural models in implicit and reduced order forms.

Aerodynamic Model Identification of a Quadrotor Subjected to Rotor Failures in the High-Speed Flight Regime

This letter presents a high-fidelity aerodynamic model of a quadrotor in the high-speed flight, with the normal configuration, or subjected to rotor failures. A novel experimental setup, data processing, and model identification procedure are developed. The main idea is that by first establishing the thrust and torque model from static wind tunnel tests as the benchmark of the aerodynamic model, and then identifying the in-plane forces, pitch and roll moments of each rotor from the free flight data obtained in a large-scale open-jet wind tunnel. The validation results show a decent model performance in predicting forces/moments, lateral forces effects, and the quadrotor trimming curve, by comparing with the benchmark model.

Aerodynamic model identification of an autonomous aircraft for airborne wind energy

SummaryAirborne wind energy (AWE) refers to a novel technology capable of harvesting energy from wind by flying crosswind patterns with tethered autonomous aircraft. Successful design of flight controllers for AWE systems relies on the availability of accurate mathematical models. Due to an expected nonconventional structure of the airborne component, the system identification procedure must be ultimately addressed via an intensive flight test campaign to gain additional insight about the aerodynamic properties. In this paper, the longitudinal dynamics of a rigid‐wing, high lift, autonomous aircraft for AWE are identified from experimental data obtained within flight tests. The aerodynamic characteristics are estimated via an efficient time‐domain multiple experiments model‐based parameter estimation algorithm.

Multi-Objective Optimization for Wind Estimation and Aircraft Model Identification

In this paper, a novel method for aerodynamic model identification of a micro air vehicle is proposed. The principal contribution is a technique of wind estimation that provides information about the existing wind during flight when no air-data sensors are available. The estimation technique employs multi-objective optimization algorithms that use identification errors to propose the wind-speed components that best fit the dynamic behavior observed. Once the wind speed is estimated, the flight experimentation data are corrected and used to perform an identification of the aircraft model parameters. A multi-objective optimization algorithm is also used, but with the objective of estimating the aerodynamic stability and control derivatives. Employing data from different flights offers the possibility of obtaining sets of models that form the Pareto fronts. Deciding which model best adjusts to the experiments performed (compromise model) will be the ultimate task of the control engineer.

Linear Aerodynamic Model Identification of a Flapping Wing MAV Based on Flight Test Data

This paper presents an approach to the system identification of the Delfly II Flapping Wing Micro Air Vehicle (FWMAV) using flight test data. It aims at providing simple FWMAV aerodynamic models that can be used in simulations as well as in nonlinear flight control systems. The undertaken methodology builds on normal aircraft system identification methods and extends these with techniques that are specific to FWMAV model identification. The entire aircraft model identification cycle is discussed covering the set-up and automatic execution of the flight test experiments, the aircraft states, the aerodynamic forces and moments' reconstruction, the aerodynamic model structure selection, the parameter estimation and finally, the model validation. In particular, a motion capturing facility was used to record the flapper's position in time and from there compute the states and aerodynamic forces and moments that acted on it, assuming flap-averaged dynamics and linear aerodynamic model structures. It is shown that the approach leads to aerodynamic models that can predict the aerodynamic forces with high accuracy. Despite less accurate, the predictions of the aerodynamic moments still follow the general trend of the measured moments. Dynamic simulations based on the identified aerodynamic models show flight trajectories that closely match the ground truth spanning a number of flapping cycles. Finally, the dimensional aerodynamic forces and moments' coefficients of two of the identified aerodynamic models are presented.

Online Aerodynamic Model Identification Using a Recursive Sequential Method for Multivariate Splines

Avoiding high computational loads is essential to online aerodynamic model identification algorithms, which are at the heart of any model-based adaptive flight-control system. Multivariate simplex B-spline methods are excellent function approximation tools for modeling the nonlinear aerodynamics of high-performance aircraft. However, the computational efficiency of the multivariate simplex B-spline method must be improved in order to enable real-time onboard applications, for example, in adaptive nonlinear flight-control systems. In this paper, a new recursive sequential identification strategy is proposed for the multivariate simplex B-spline method aimed at increasing its computational efficiency, thereby allowing its use in onboard system identification applications. The main contribution of this new method is a significant reduction of the computational load for large-scale online identification problems as compared to the existing multivariate simplex B-spline methods. The proposed method consists of two sequential steps for each time interval and makes use of a decomposition of the global problem domain into a number of subdomains, called modules. In the first step, the B-coefficients for each module are estimated using a least-squares estimator. In the second step, the local B-coefficients for each module are then smoothened into a single global B-coefficient vector using a linear minimum mean-square errors estimation. The new method is compared to existing batch and recursive multivariate simplex B-spline methods in a numerical experiment in which an aerodynamic model is recursively identified based on data from a NASA F-16 wind-tunnel model.

Multi-Degree-of-Freedom Wind-Tunnel Maneuver Rig for Dynamic Simulation and Aerodynamic Model Identification

A new five-degree-of-freedom rig for the dynamic wind-tunnel testing of aircraft models has been developed. The maneuver rig enables a large set of conventional and more-extreme aircraft maneuvers to be performed in the controlled environment of a wind tunnel, allowing direct physical simulation of in-flight maneuvers and the identification of aerodynamic models from aircraft-model time histories. A mathematical model of the rig has been developed for numerical simulation and identification purposes. The development of a quasi-steady aerodynamic model of the longitudinal motion for a subscale test aircraft is presented to illustrate rig capabilities. The longitudinal modes of motion are excited by a remotely controlled aircraft-model stabilator and dynamic-rig aerodynamic compensator deflections. Two rig configurations are considered: aircraft-model pitch only and aircraft-model pitch with heave, which is implemented via rig-pitch motion. The aircraft-model tail and wing in the mathematical model are considered separately to assist in identifying and pitch-rate stability derivatives. The results of parameter estimation are presented with analysis of their accuracy. Last, to further explore the rig’s potential, physical simulation time histories from three experiments using two, three, and four of the available degrees of freedom are presented and discussed.

Fuzzy-Logic Modeling of a Rolling Unmanned Vehicle in Antarctica Wind Shear

of wind shear are investigated using the recorded test data during a flight from the Pegasus white ice runway in Antarctica. The main purpose of this paper is to determine the reasons and characteristics of roll oscillations for improvement of autopilot design and ground operator training. The Meridian flight test is conducted with the necessaryrangeconstraint, which precludes steadylevel flight.The measured orestimated data,such as theangle of attack and sideslip angle, are filtered through a nonlinear compatibility analysis. The estimated aerodynamic coefficients are modeled as implicit functions of filtered flight variables in a concept of aerodynamic model identification, instead of parameter identification. A model identification method called fuzzy-logic modeling is used to set up the aerodynamic models without assuming the functional relations. The aircraft’s oscillatory stability and control derivatives are found to be significantly impacted by the environmental disturbances. The exhibited highly oscillatory motion in the manual-control mode is analyzed and identified as the pilot-induced oscillation with a new energy method. It is shown that the pilot-induced oscillation is identified as the motion when the motion energy is increasedbythecontrolinput.Intheautopilot flight,themotionisdeterminedtobewingrock,whichisexcitedbythe pilot-induced oscillation in the manual mode and sustained by the interaction between the wing wake and the V tail.

Actuator Fault Detection by Aerodynamic Model Identification

This paper presents a Fault Detection and Diagnosis (FDD) method for detecting faults in control surface actuators. Due to a lack of “local” measurements directly related to the actuator and control surface, detection of certain type of faults is very hard. The method introduced here makes use of the knowledge of the aircraft's dynamics to achieve fault detection and isolation. E.g., an aileron fault, the rolling moment of the aircraft is modeled. The parameters of the model are identified online, and if a fault occurs, a change in these parameters is observed. By evaluating these changes the faults can be detected and isolated. The advantage of this method is that it can detect different types of faults (sensor bias, jamming, disconnection) with only one method.

Modeling and Model Reference Adaptive Control of Aircraft with Asymmetric Damage

This paper addresses some fundamental issues in adaptive control of aircraft with struc- tural damage. It presents a thorough study of linearized aircraft models with damage to obtain new details of system descriptions, such as coupling and partial derivatives of lateral and longitudinal dynamics. A detailed study of system invariance under damage conditions is performed for generic aircraft models to obtain key system characterizations for model reference adaptive control (MRAC), such as infinite zero structure and signs of high frequency gain matrices. A comprehensive study of multivariable MRAC systems in the presence of damage is performed to obtain critical design specifications for adaptive flight control, such as system and controller parametrizations and adaptive parameter up- date laws. Both analytical and simulation results are given to illustrate the design and performance of adaptive control systems for aircraft flight control. Adaptive control of aircraft in the presence of damage has been an important topic in the research of flight control design for aircraft safety. Damage can cause uncertain parametric and structural variations, which requires new aircraft modeling and control approaches. In Reference (1), a study of aircraft dynamics with damage is presented, and a neural network based adaptive control algorithm is introduced for control of aircraft in the presence of structure uncertainties. In (2), equations of motion are introduced in detail for aircraft with asymmetric mass loss. In (3), we introduced a nonlinear aircraft model with partial wing damage, and illustrated linearization of such a model. In (4), real time identification of a damaged aircraft model is studied. A two-step identification process is introduced, which consists of an aircraft state estimation phase and an aerodynamic model identification step. With such a two-step process, the nonlinear part of the model identification is isolated in the first phase, and the aerodynamic parameter identification procedure is simplified to a linear one. A hybrid adaptive control method is proposed in (5) for control of aircraft with damage. The control design is based on a neural network parameter estimation blended with a direct adaptive law. A stability and convergence analysis is presented for this adaptive control methodology. For accommodating unknown changes in the structure and parameters, multivariable MRAC designs offer many advantages. In (6), we introduced an MRAC design based on the LDS decomposition of the high frequency gain matrix for the control of aircraft with multiple wing damage. The key design conditions are that, both the nominal and post-damage systems should have a uniform known modified interactor matrix, and the leading principal minors of their high frequency gain matrices should be nonzero with their signs unchanged. In (7), we studied linearization of nonlinear aircraft models under damage conditions and designed a multivariable MRAC scheme which does not require the knowledge of the signs of the high frequency gain matrix. Potential extension to aircraft flight control systems with changing signs of the high frequency gain matrix remains a topic of future research.

Unmanned aerial vehicle aerodynamic model identification from a racetrack manoeuvre

The determination of aircraft stability and control derivatives from wind-tunnel and computational fluid dynamics data can be an expensive and time-consuming procedure. This case study shows how linear system identification techniques can be used to obtain this information with sufficient accuracy to design a satisfactory flight control system for an aerial target of the Royal Thai Air Force. Just one flight was undertaken, using a racetrack manoeuvre, to provide the data for identification and validation. The system parameters were identified and a flight control system was designed. Hardware-in-the-loop simulation was used to perform initial tests on the controller and to test the control system hardware and software. A second flight was performed to test the resulting controller, and a satisfactory performance was obtained without the need to adjust the controller gains.

Advances in Real-Time Aerodynamic Model Identification

We describe the development of an in-flight, near real-time, aerodynamic model identification (RAMI) technique, for fixed-wing aircraft. This technique can be used to develop and validate an aerodynamic model to the Federal Aviation Administration AC 120 Level D flight simulator aerodynamic model criteria; and initial demonstrations of its capabilities are taking place on the National Research Council Falcon 20 research aircraft. As part of the RAMI development, innovative, self-contained software algorithms and hardware processing have been designed for onboard use. As well, novel hardware and software techniques are being applied to calibrate fundamental in-flight measurements, such as air data, in real time. The RAMI technique uses the 2-3-1-1 (alternating control input steps in seconds) maneuver, executed in each of the pitch, roll and yaw axes. These maneuvers are flown in flight conditions that span the entire flight envelope of interest. At each flight condition, the small perturbation stability and control derivatives (point model) are determined in real time. An automatic curve-fit process then combines the point models to develop a global aerodynamic model of the aircraft

On-Line Identification of Aircraft Aerodynamic Model Parameters

Abstract Accurate mathematical models of aircraft aerodynamics find their use in flight simulation, automatic flight control and aircraft operation. Unknown parameters In these models can be extracted from flight test data by means of a variety of system identification techniques. This paper shows how on-line aerodynamic model identification has become feasible by applying algorithms based on a decomposition of the state and parameter estimation problem and high speed multi-processor computer systems. Results from a recent flight test program are presented.

High-Alpha Aerodynamic Model Identification of T-2C Aircraft Using the EBM Method

The estimation-before-modeling (EBM) system identification method has been applied to actual highalpha/beta flight data of the T-2C jet trainer aircraft. Eighteen maneuvers (600 s at 20 Hz) were processed by the two steps of the EBM technique. The estimated biases and scale factors of the measurements are presented. Comparisons between the wind-tunnel and the identified state and control derivatives are given. Comparisons between the predicted and the identified dynamic derivatives are presented. There is good agreement between the identified nonlinear model and the wind-tunnel model. Many (although not all) identified dynamic derivatives are in agreement with the prediction models except near stall or at high alpha.