Robust Aerodynamic Model Identification: A New Method for Aircraft System Identification In the Presence of General Dynamic Model Deficiencies

2019 ◽  
Author(s):  
Gregory J. Moszczynski ◽  
Jordan M. Leung ◽  
Peter R. Grant
Keyword(s):  
System Identification ◽  
Dynamic Model ◽  
Model Identification ◽  
New Method ◽  
Aerodynamic Model ◽  
Aircraft System ◽  
General Dynamic

System identification of a vortex lattice aerodynamic model

AIAA Journal ◽  
2002 ◽  
Vol 40 ◽  
pp. 1187-1196
Author(s):  
J.-N. Juang ◽  
D. Kholodar ◽  
E. H. Dowell
Keyword(s):  
System Identification ◽  
Vortex Lattice ◽  
Aerodynamic Model

METHOD FOR ESTIMATING ERRORS IN MEASURING INSTRUMENTS BY THE SPACE CATALOGUE ORBITS

2018 ◽  
pp. 76-84
Author(s):  
K. V. Sorokin ◽  
E. A. Sunarchina
Keyword(s):  
Dynamic Model ◽  
New Method ◽  
Measuring Instruments ◽  
Measuring Instrument ◽  
Software Complex

Improvement of orbits precision is one of the most important tasks of space surveillance catalogue maintenance. The solution of this problem is directly related to an adequate consideration of the errors of the coordinate information from the measuring instruments. The article consideresd a new method for estimating the precision of measuring instruments on the catalog orbits. To carry out such analysis, in PJSC «VIMPEL» special technological program was created. Main results of a study of radar errors with orbits of space surveillance catalogue was presented. Also, the results were compared with data of measuring instrument's calibration software complex. This software complex provides determination of satellite's position with errors less than 10 m. A new dynamic model of measuring instrument errors is proposed.

Dynamic Model Identification of Collaborative Robots Using a Three-Loop Iterative Method

2021 ◽  
Author(s):  
Jie Deng ◽  
Weiwei Shang ◽  
Bin Zhang ◽  
Shengchao Zhen ◽  
Shuang Cong
Keyword(s):  
Iterative Method ◽  
Dynamic Model ◽  
Model Identification ◽  
Collaborative Robots

A new imputation-based incomplete data-driven fuzzy modeling for accuracy improvement in ubiquitous computing applications

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sonia Goel ◽  
Meena Tushir
Keyword(s):  
Parameter Estimation ◽  
Missing Data ◽  
Incomplete Data ◽  
Model Identification ◽  
Fuzzy Model ◽  
New Method ◽  
Content Type ◽  
Ubiquitous Environment ◽  
Sugeno Model ◽  
Takagi Sugeno

Purpose In real-world decision-making, high accuracy data analysis is essential in a ubiquitous environment. However, we encounter missing data while collecting user-related data information because of various privacy concerns on account of a user. This paper aims to deal with incomplete data for fuzzy model identification, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features. Design/methodology/approach In this work, authors proposed a three-fold approach for fuzzy model identification in which imputation-based linear interpolation technique is used to estimate missing features of the data, and then fuzzy c-means clustering is used for determining optimal number of rules and for the determination of parameters of membership functions of the fuzzy model. Finally, the optimization of the all antecedent and consequent parameters along with the width of the antecedent (Gaussian) membership function is done by gradient descent algorithm based on the minimization of root mean square error. Findings The proposed method is tested on two well-known simulation examples as well as on a real data set, and the performance is compared with some traditional methods. The result analysis and statistical analysis show that the proposed model has achieved a considerable improvement in accuracy in the presence of varying degree of data incompleteness. Originality/value The proposed method works well for fuzzy model identification method, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features with varying degree of missing data as compared to some well-known methods.

Rotorcraft control response using linearised and non-linear flight dynamic models with different inflow models

2017 ◽  
Vol 121 (1238) ◽  
pp. 553-575 ◽  
Author(s):  
T. Sakthivel ◽  
C. Venkatesan
Keyword(s):  
Dynamic Model ◽  
Dynamic Models ◽  
Model Identification ◽  
Flight Test ◽  
Step Size ◽  
Response Characteristics ◽  
Wide Range ◽  
Non Linear ◽  
Steady Level ◽  
Control Response

ABSTRACTThe aim of the present study is to develop a relatively simple flight dynamic model which should have the ability to analyse trim, stability and response characteristics of a rotorcraft under various manoeuvring conditions. This study further addresses the influence of numerical aspects of perturbation step size in linearised model identification and integration timestep on non-linear model response. In addition, the effects of inflow models on the non-linear response are analysed. A new updated Drees inflow model is proposed in this study and the applicability of this model in rotorcraft flight dynamics is studied. It is noted that the updated Drees inflow model predicts the control response characteristics fairly close to control response characteristics obtained using dynamic inflow for a wide range of flight conditions such as hover, forward flight and recovery from steady level turn. A comparison is shown between flight test data, the control response obtained from the simple flight dynamic model, and the response obtained using a more detailed aeroelastic and flight dynamic model.

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