Identification of Physical Helicopter Models Using Subspace Identification

2020 ◽  
Vol 65 (2) ◽  
pp. 1-14
Author(s):  
Sevil Avcıoğlu ◽  
Ali Türker Kutay ◽  
Kemal Leblebicioğlu

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.

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Ya Gu ◽  
Quanmin Zhu ◽  
Jicheng Liu ◽  
Peiyi Zhu ◽  
Yongxin Chou

This paper presents a multi-innovation stochastic gradient parameter estimation algorithm for dual-rate sampled state-space systems with d-step time delay by the multi-innovation identification theory. Considering the stochastic disturbance in industrial process and using the gradient search, a multi-innovation stochastic gradient algorithm is proposed through expanding the scalar innovation into an innovation vector in order to obtain more accurate parameter estimates. The difficulty of identification is that the information vector in the identification model contains the unknown states. The proposed algorithm uses the state estimates of the observer instead of the state variables to realize the parameter estimation. The simulation results indicate that the proposed algorithm works well.


Author(s):  
Jeffrey F. Monaco ◽  
David S. Kidman ◽  
Randall L. Bickford ◽  
Donald J. Malloy

The US Air Force’s two main aeropropulsion test centers, Arnold Engineering Development Center and the Air Force Flight Test Center, are developing a common suite of modeling and simulation tools employing advanced predictive modeling technologies. This common set of modeling and simulation tools incorporates real-time data validation, system identification, parameter estimation model calibration, and automated model updating as new test results or operational data become available. The expected benefit is improved efficiency and accuracy for online diagnostic monitoring of Air Force assets. These resultant models could also be used for flight manual development, determining compliance to specifications, or to aid in real-time equipment monitoring. This paper describes the integrated approach to system identification, parameter estimation, and model updating. Implementation of a software package to enable efficient model handoff between test groups and centers is discussed. An F/A-22 inlet model is used to demonstrate the approach. Compact polynomial function models of the distortion and recovery flow descriptors and 40-probe pressure values are derived from quasi-steady and instantaneous subscale wind tunnel data. The model parameters are then calibrated with F/A-22 flight test data. Results show that the modeling algorithm captures the relevant nonlinear physics of the application, and the calibration and updating procedure improves the model match to flight data. A companion paper provides preliminary results from integrating the calibrated total-pressure inlet distortion and recovery models into a real-time equipment health monitoring system to support test operations.


2006 ◽  
Vol 20 (8) ◽  
pp. 1224-1231 ◽  
Author(s):  
Wook-Je Park ◽  
Eung-Tai Kim ◽  
Kie-Jeong Seong ◽  
Yeong-Cheol Kim

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