Aerothermodynamic parameter estimation from Shuttle thermocouple data during transient flight test maneuvers

. 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.

Download Full-text

Identification of Physical Helicopter Models Using Subspace Identification

Journal of the American Helicopter Society ◽

10.4050/jahs.65.022001 ◽

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.

Download Full-text

Uses of parameter estimation in flight test

Journal of Aircraft ◽

10.2514/3.48210 ◽

1983 ◽

Vol 20 (12) ◽

pp. 1043-1049 ◽

Cited By ~ 1

Author(s):

Kenneth W. Iliff ◽

Richard E. Maine

Keyword(s):

Parameter Estimation ◽

Flight Test

Download Full-text

Non-linear aerodynamic modelling of unmanned cropped delta configuration from experimental data

The Aeronautical Journal ◽

10.1017/aer.2016.124 ◽

2017 ◽

Vol 121 (1237) ◽

pp. 320-340 ◽

Cited By ~ 7

Author(s):

S. Saderla ◽

R. Dhayalan ◽

A.K. Ghosh

Keyword(s):

Parameter Estimation ◽

Flow Separation ◽

Linear Models ◽

Rectangular Cross Section ◽

Flight Test ◽

Point Mapping ◽

Flight Data ◽

Aerodynamic Model ◽

Non Linear ◽

High Angles Of Attack

ABSTRACTThe paper presents the aerodynamic characterization of a low-speed unmanned aerial vehicle, with cropped delta planform and rectangular cross section, at and around high angles-of-attack using flight test methods. Since the linear models used for identification from flight data at low and moderate angles of attack become unsuitable for accurate parameter estimation at high angles of attack, a non-linear aerodynamic model has to be considered. Therefore, the Kirchhoff's flow separation model was used to incorporate the non-linearity in the aerodynamic model in terms of flow separation point and stall characteristic parameters. The Maximum Likelihood (ML) and Neural Gauss-Newton (NGN) methods were used to perform the parameter estimation on one set of low angle-of-attack and one set of near-stall flight data. It is evident from the estimates that the NGN method, which does not involve solving equations of motion, performs on a par with the classical ML method. This may be attributed to the reason that NGN method uses a neural network which has been trained by performing point to point mapping of the measured flight data. This feature of NGN method enhances its application over a wider envelope of high angles of attack flight data.

Download Full-text

Longitudinal parameter estimation from real flight data of unmanned cropped delta flat plate configuration

International Journal of Intelligent Unmanned Systems ◽

10.1108/ijius-07-2015-0008 ◽

2016 ◽

Vol 4 (1) ◽

pp. 2-22 ◽

Cited By ~ 7

Author(s):

Subrahmanyam Saderla ◽

Dhayalan R ◽

Ajoy Kanti Ghosh

Keyword(s):

Parameter Estimation ◽

Maximum Likelihood ◽

Rectangular Cross Section ◽

Flight Test ◽

Least Square ◽

Likelihood Method ◽

Content Type ◽

Flight Data ◽

Aerodynamic Model ◽

Estimated Parameters

Purpose – The purpose of this paper is to describe the longitudinal aerodynamic characterization of an unmanned cropped delta configuration from real flight data. In order to perform this task an unmanned configuration with cropped delta planform and rectangular cross-section has been designed, fabricated, instrumented and flight tested at flight laboratory in Indian Institute of Technology Kanpur (IITK), India. Design/methodology/approach – As a part of flight test program a real flight database, through various maneuvers, have been generated for the designed unmanned configuration. A dedicated flight data acquisition system, capable of onboard logging and telemetry to ground station, has been used to record the flight data during these flight test experiments. In order to identify the systematic errors in the measurements, the generated flight data has been processed through data compatibility check. Findings – It is observed from the flight path reconstruction that the obtained biases are negligible and the scale factors are almost close to unity. The linear aerodynamic model along with maximum likelihood and least-square methods have been used to perform the parameter estimation from the obtained compatible flight data. The lower values of Cramer-Rao bounds obtained for various parameters has shown significant confidence in the estimated parameters using maximum likelihood method. In order to validate the aerodynamic model used and to increase the confidence in the estimated parameters a proof-of-match exercise has been carried out. Originality/value – The entire work presented is original and all the experiments have been carried out in Flight laboratory of IITK.

Download Full-text