scholarly journals Generalized Attitude Model Identification of a Quadrotor

2021 ◽  
Vol 1887 (1) ◽  
pp. 012017
Author(s):  
Qiao-Hui Qin ◽  
Jin-Cang Liu ◽  
Li-Hui Geng
Keyword(s):  
Model Identification ◽  
Attitude Model

Attitude model identification of on‐orbit satellites

2009 ◽  
Vol 81 (2) ◽  
pp. 149-154 ◽  
Author(s):  
Lihui Geng ◽  
Tao Zhang ◽  
Deyun Xiao ◽  
Jingyan Song
Keyword(s):  
Model Identification ◽  
Attitude Model

Attitude Model Identification for Sub-Mini Dual Ducted UAV under Hovering

2014 ◽  
Vol 513-517 ◽  
pp. 2812-2815
Author(s):  
Tong Yue Gao ◽  
Dong Dong Wang ◽  
Fei Tao ◽  
Hai Lang Ge
Keyword(s):  
System Identification ◽  
Model Identification ◽  
Dynamics Analysis ◽  
Mechanism Analysis ◽  
Subspace Method ◽  
Output Variable ◽  
Unknown Parameters ◽  
The Real ◽  
Attitude Model ◽  
Identification Model

Based on the sub-mini dual ducted UAV dynamics analysis of Newton, and according to the characteristics of the hovering and variable posture mechanism, this paper combined methods of mechanism analysis with system identification to get the attitude model of under actuated sub-mini dual ducted UAV, which contained input variable, output variable and some unknown parameters. Then the subspace method used the real flying data to identify above the attitude model. Finally，the correctness of the identification model was verified.

PI(D) tuning with contextual model identification

2009 ◽  
Author(s):  
Alberto Leva ◽  
Sara Negro ◽  
Alessandro Vittorio Papadopoulos
Keyword(s):  
Model Identification ◽  
Contextual Model

Model Fungsi Transfer Input Ganda untuk Pemodelan Jakarta Islamic Index

2019 ◽  
Vol 7 (3) ◽  
Author(s):  
Nur Laela Fitriani ◽  
Pika Silvianti ◽  
Rahma Anisa
Keyword(s):  
Exchange Rate ◽  
Transfer Function ◽  
Multivariate Time Series ◽  
Model Identification ◽  
Transfer Function Model ◽  
Function Model ◽  
Jones Index ◽  
Multiple Input ◽  
Identification Stage ◽  
Stage Estimation

Transfer function model with multiple input is a multivariate time series forecasting model that combines several characteristics of ARIMA models by utilizing some regression analysis properties. This model is used to determine the effect of output series towards input series so that the model can be used to analyze the factors that affect the Jakarta Islamic Index (JII). The USD exchange rate against rupiah and Dow Jones Index (DJI) were used as input series. The transfer function model was constructed through several stages: model identification stage, estimation of transfer function model, and model diagnostic test. Based on the transfer function model, the JII was influenced by JII at the period of one and two days before. JII was also affected by the USD exchange rate against rupiah at the same period and at one and two days before. In addition, the JII was influenced by DJI at the same period and also at period of one until five days ago. The Mean Absolute Prencentage Error (MAPE) value of forecasting result was 0.70% and the correlation between actual and forecast data was 0.77. This shows that the model was well performed for forecasting JII.

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