Flight Test Maneuver Design and Aerodynamic Parameter Estimation for Single Use Autonomous Gliding Air Vehicles

2017 ◽  
Author(s):  
Vefa N. Yavuztürk ◽  
Eren Topbas ◽  
Ümit Kutluay ◽  
Yigit Yazicioglu
Keyword(s):  
Parameter Estimation ◽  
Flight Test ◽  
Aerodynamic Parameter ◽  
Single Use ◽  
Air Vehicles

Lateral directional aircraft aerodynamic parameter estimation using adaptive stochastic nonlinear filter

2021 ◽  
Vol 125 (1294) ◽  
pp. 2217-2228
Author(s):  
M. Mohamed ◽  
N. Joy
Keyword(s):  
Parameter Estimation ◽  
Real Time ◽  
Adaptive Filter ◽  
Estimation Method ◽  
Flight Test ◽  
Nonlinear Filter ◽  
Covariance Matrices ◽  
Directional Derivatives ◽  
Aerodynamic Parameter ◽  
Noise Covariance

AbstractThis paper aims to accurately estimate the lateral directional aerodynamic parameters in real time irrespective of the variations in the process and measurement covariance matrices. The proposed algorithm for parameter estimation is based on the integration of adaptive techniques into a stochastic nonlinear filter. The proposed adaptive estimation algorithm is applied to flight test data, and the lateral directional derivatives are estimated in real time. The estimates are compared with those obtained from the Filter Error Method (FEM), an offline parameter estimation method accounting for process noise. The estimation results are observed to be very comparable, and the supremacy of the adaptive filter is illustrated by varying the covariance matrices of both process and measurement noises. The parameters estimated by the adaptive filter are found to converge to their actual values, whereas the estimates of the regular filter are observed to diverge from the actual values when changing the noise covariance matrices. The proposed adaptive algorithm can estimate the lateral directional aerodynamic derivatives more accurately without prior knowledge of either process or measurement noise covariance matrices. Hence, it is of great value in online implementations.

scholarly journals Gust Mitigation of Micro Air Vehicles Using Passive Articulated Wings

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Adetunji Oduyela ◽  
Nathan Slegers
Keyword(s):  
Experimental Validation ◽  
Micro Air Vehicles ◽  
Flight Test ◽  
Analytical Investigation ◽  
Micro Air Vehicle ◽  
Gust Alleviation ◽  
Air Vehicle ◽  
Lifting Surfaces ◽  
Gust Mitigation ◽  
Air Vehicles

Birds and insects naturally use passive flexing of their wings to augment their stability in uncertain aerodynamic environments. In a similar manner, micro air vehicle designers have been investigating using wing articulation to take advantage of this phenomenon. The result is a class of articulated micro air vehicles where artificial passive joints are designed into the lifting surfaces. In order to analyze how passive articulation affects performance of micro air vehicles in gusty environments, an efficient 8 degree-of-freedom model is developed. Experimental validation of the proposed mathematical model was accomplished using flight test data of an articulated micro air vehicle obtained from a high resolution indoor tracking facility. Analytical investigation of the gust alleviation properties of the articulated micro air vehicle model was carried out using simulations with varying crosswind gust magnitudes. Simulations show that passive articulation in micro air vehicles can increase their robustness to gusts within a range of joint compliance. It is also shown that if articulation joints are made too compliant that gust mitigation performance is degraded when compared to a rigid system.

Drag Coefficient Identification from Flight Data via Optimal Observer

2014 ◽  
Vol 687-691 ◽  
pp. 787-790
Author(s):  
Rong Jun Yang ◽  
Yao Ye
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Drag Coefficient ◽  
Dynamic Equation ◽  
Unscented Kalman Filter ◽  
Flight Test ◽  
Measurement Noise ◽  
Estimation Technique ◽  
Flight Data ◽  
Coefficient Identification

. For effectively using flight test data to extract drag coefficient, an optimal observer based on parameter estimation technique is proposed. The point mass dynamic equation is used to form the Unscented Kalman Filter (UKF) and the smoother (URTSS) for the estimation of a projectile’s flight states. The projectile flight states are then solved and utilized to extract the drag coefficient information using the observer techniques. The simulation verifies the feasibility of the method: with measurement noise, the accurate drag coefficient is obtained by using the smoother.

Identification of Physical Helicopter Models Using Subspace Identification

2020 ◽  
Vol 65 (2) ◽  
pp. 1-14
Author(s):  
Sevil Avcıoğlu ◽  
Ali Türker Kutay ◽  
Kemal Leblebicioğlu
Keyword(s):  
Parameter Estimation ◽  
State Space ◽  
Nonlinear Optimization ◽  
Physical Parameter ◽  
Test Data ◽  
Flight Test ◽  
The State ◽  
Physical Parameters ◽  
Subspace Identification ◽  
Study Results

Subspace identification is a powerful tool due to its well-understood techniques based on linear algebra (orthogonal projections and intersections of subspaces) and numerical methods like singular value decomposition. However, the state space model matrices, which are obtained from conventional subspace identification algorithms, are not necessarily associated with the physical states. This can be an important deficiency when physical parameter estimation is essential. This holds for the area of helicopter flight dynamics, where physical parameter estimation is mainly conducted for mathematical model improvement, aerodynamic parameter validation, and flight controller tuning. The main objective of this study is to obtain helicopter physical parameters from subspace identification results. To achieve this objective, the subspace identification algorithm is implemented for a multirole combat helicopter using both FLIGHTLAB simulation and real flight-test data. After obtaining state space matrices via subspace identification, constrained nonlinear optimization methodologies are utilized for extracting the physical parameters. The state space matrices are transformed into equivalent physical forms via the "sequential quadratic programming" nonlinear optimization algorithm. The required objective function is generated by summing the square of similarity transformation equations. The constraints are selected with physical insight. Many runs are conducted for randomly selected initial conditions. It can be concluded that all of the significant parameters can be obtained with a high level of accuracy for the data obtained from the linear model. This strongly supports the idea behind this study. Results for the data obtained from the nonlinear model are also evaluated to be satisfactory in the light of statistical error analysis. Results for the real flight-test data are also evaluated to be good for the helicopter modes that are properly excited in the flight tests.

scholarly journals Leading-Edge Flow Sensing for Aerodynamic Parameter Estimation

AIAA Journal ◽  
2018 ◽  
Vol 56 (12) ◽  
pp. 4706-4718 ◽  
Author(s):  
Aditya Saini ◽  
Ashok Gopalarathnam
Keyword(s):  
Parameter Estimation ◽  
Leading Edge ◽  
Aerodynamic Parameter ◽  
Flow Sensing ◽  
Edge Flow
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