A Novel Updating Algorithm for Linearized State-Space Models of an Unmanned Flexible Aircraft Using Flight Test Data

2022 ◽  
Author(s):  
Özge Süelözgen
Keyword(s):  
State Space ◽  
Test Data ◽  
Flight Test ◽  
State Space Models ◽  
Flexible Aircraft

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.

Flexible Aircraft Simulation Validation with Flight Test Data

AIAA Journal ◽  
2021 ◽  
pp. 1-20
Author(s):  
Antônio B. Guimarães Neto ◽  
Guilherme C. Barbosa ◽  
Juliano A. Paulino ◽  
Rafael M. Bertolin ◽  
Jéssica S. M. Nunes ◽  
...  
Keyword(s):  
Test Data ◽  
Flight Test ◽  
Flexible Aircraft ◽  
Simulation Validation

Parameter Estimation Using a Modified Version of SPSA Algorithm Applied to State Space Models

2009 ◽  
Vol 129 (12) ◽  
pp. 1187-1194 ◽  
Author(s):  
Jorge Ivan Medina Martinez ◽  
Kazushi Nakano ◽  
Kohji Higuchi
Keyword(s):  
Parameter Estimation ◽  
State Space ◽  
State Space Models

Estimation of control surface dynamic derivatives from flight test data

1972 ◽  
Author(s):  
B. HALL ◽  
M. SHOLAR ◽  
A. ALLRED
Keyword(s):  
Test Data ◽  
Flight Test ◽  
Control Surface ◽  
Surface Dynamic

Stall Buffet Modeling using Swept Wing Flight Test Data

2021 ◽  
Author(s):  
Sven Marschalk ◽  
Peter C. Luteijn ◽  
Dirk van Os ◽  
Daan M. Pool ◽  
Coen C. de Visser
Keyword(s):  
Test Data ◽  
Flight Test ◽  
Swept Wing

Heat transfer modeling and identification using fractional order state space models

2008 ◽  
Vol 42 (6-8) ◽  
pp. 939-951 ◽  
Author(s):  
Tounsia Jamah ◽  
Rachid Mansouri ◽  
Saïd Djennoune ◽  
Maâmar Bettayeb
Keyword(s):  
Heat Transfer ◽  
State Space ◽  
Fractional Order ◽  
State Space Models ◽  
Heat Transfer Modeling ◽  
Order State
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