Physical subspace identification for helicopters

Date

2019

Author

Avcıoğlu, Sevil

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

3
views

0
downloads

Cite This

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 QR and 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. In order to achieve this objective, N4SID subspace identification algorithm is implemented for a multi-role 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 both “Sequential Quadratic Programming” and “Interior Point” nonlinear optimization algorithms. 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 parameters with high significance 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.

Subject Keywords

Helicopters, Helicopters Aerodynamics., Subspace Identification, Parameter Estimation, Similarity Transformation, Optimization, Helicopter Dynamics.

URI

http://etd.lib.metu.edu.tr/upload/12623229/index.pdf
https://hdl.handle.net/11511/43399

Collections

Graduate School of Natural and Applied Sciences, Thesis