On estimation of vehicle linear model parameters

Tayefi, Morteza; Roshanian, Jafar; Ghaffari, Ali

doi:10.1108/00022660910983716

Cited by 4 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The traditional methods, obtain some aerodynamic coefficients from FDR data by application of parameter estimation methods, and then they use the coefficients in the transfer functions. Then the functions are perturbed, and finally, the flight characteristics are taken out such as methods used by Jategaonkar et al (2004), Tischler and Remple (2006) and Tayefi et al (2009). The "flight dynamic mode" or the "dynamic flight characteristics" are the information that represents the status of the flights.…”

Section: Introductionmentioning

confidence: 99%

Spin flight mode identification with OEEMD algorithm

Mokhtari

Sabzehparvar

2019

AEAT

View full text Add to dashboard Cite

Purpose The paper aims to present an innovative method for identification of flight modes in the spin maneuver, which is highly nonlinear and coupled dynamic. Design/methodology/approach To fix the mode mixing problem which is mostly happen in the EMD algorithm, the authors focused on the proposal of an optimized ensemble empirical mode decomposition (OEEMD) algorithm for processing of the flight complex signals that originate from FDR. There are two improvements with the OEEMD respect to the EEMD. First, this algorithm is able to make a precise reconstruction of the original signal. The second improvement is that the OEEMD performs the task of signal decomposition with fewer iterations and so with less complexity order rather than the competitor approaches. Findings By applying the OEEMD algorithm to the spin flight parameter signals, flight modes extracted, then with using systematic technique, flight modes characteristics are obtained. The results indicate that there are some non-standard modes in the nonlinear region due to couplings between the longitudinal and lateral motions. Practical implications Application of the proposed method to the spin flight test data may result accurate identification of nonlinear dynamics with high coupling in this regime. Originality/value First, to fix the mode mixing problem in EMD, an optimized ensemble empirical mode decomposition algorithm is introduced, which disturbed the original signal with a sort of white Gaussian noise, and by using white noise statistical characteristics the OEEMD fix the mode mixing problem with high precision and fewer calculations. Second, by applying the OEEMD to the flight output signals and with using the systematic method, flight mode characteristics which is very important in the simulation and controller designing are obtained.

show abstract

Section: Introductionmentioning

confidence: 99%

Spin flight mode identification with OEEMD algorithm

Mokhtari

Sabzehparvar

2019

AEAT

View full text Add to dashboard Cite

show abstract

“…Second, the characteristics of dynamic modes can be extracted indirectly from the flight measurements after identification of aerodynamic derivatives and solving the longitudinal and lateral characteristic equation. These derivatives are identified by parameter estimation methods, such as Morelli (2000;Jategaonkar et al, 2004;Tischler and Remple, 2006;Tayefi et al, 2009). As parameter estimation methods have their own errors and impose additional time and computational costs, it is more desirable to obtain characteristics of the flight modes, in a direct manner.…”

Section: Introductionmentioning

confidence: 99%

Estimation of flight modes with Hilbert-Huang transform

Bagherzadeh

Sabzehparvar

2015

Aircraft Eng & Aerospace Tech

View full text Add to dashboard Cite

Purpose -This paper aims to present a new method for identification of some flight modes, including natural and non-standard modes, and extraction of their characteristics, directly from measurements of flight parameters in the time domain. Design/methodology/approach -The Hilbert-Huang transform (HHT), as a novel prevailing tool in the signal analysis field, is used to attain the purpose. The study shows that the HHT has superior potential capabilities to improve the airplane flying quality analysis and to conquer some drawbacks of the classical method in flight dynamics. Findings -The proposed method reveals the existence of some non-standard modes with small damping ratios at non-linear flight regions and obtains their characteristics.Research limitations/implications -The paper examines only airplane longitudinal dynamics. Further research is needed regarding lateral-directional dynamic modes and coupling effects of the longitudinal and lateral modes. Practical implications -Application of the proposed method to the flight test data may result in real-time flying quality analysis, especially at the non-linear flight regions. Originality/value -First, to utilize the empirical mode decomposition (EMD) capabilities in real time, a local-online algorithm is introduced which estimates the signal trend by the Savitzky-Golay sifting process and eliminates it from the signal in the EMD algorithm. Second, based on the local-online EMD algorithm, a systematic method is proposed to identify flight modes from flight parameters in the time domain.

show abstract