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.

scholarly journals Forecasting the Items Consumption in the Hotel Storage with the Autoregressive Integrated Moving Average Method

2021 ◽  
Vol 3 (1) ◽  
pp. 13-19
Author(s):  
Christopher Chandra ◽  
Alfannisa Annurrullah Fajrin ◽  
Cosmas Eko Suharyanto
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Arima Model ◽  
Model Identification ◽  
Minimum Amount ◽  
Model Estimation ◽  
Output Variable ◽  
Autoregressive Integrated Moving Average ◽  
Identification Model

In this era, hotel has storage as a storing space for every kind of items. Items stored in the storage are items being used for the needs of the staffs, also for the needs of hotel’s operational. The item consumption is running smoothly with resupply. However, there are often mistakes in resupplying the items. For preventing those several mistakes, a reference is needed to be used for controlling the amount of items arrival (monthly) with minding the amount of items in the storage should be. The reference to be used is the forecast of the item consumption every month. Forecasting was being done with Autoregressive Integrated Moving Average (ARIMA) method. There are five steps needed to build the ARIMA model, such as plot identification, model identification, model estimation, choosing the best model, and prediction (forecast). The input variable to be used in this research is the rime series from the data of storage’s item consumption starts from January 2018 until October 2020, and the output variable is the result of the prediction of item consumption in the next period, such as in November to December 2020. The results is subtracted with the number of items left in storage to obtain the minimum amount of item to be entered for the month.

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

System Identification and Pseudo-Earthquake Excitation of a Real-Scaled 5 Story Steel Frame Structure

2008 ◽  
Author(s):  
Eunchurn Park ◽  
Sang-Hyun Lee ◽  
Sung-Kyung Lee ◽  
Hee-San Chung ◽  
Kyung-Won Min
Keyword(s):  
System Identification ◽  
Forced Vibration ◽  
Structural Information ◽  
Frame Structure ◽  
Fe Model ◽  
Building Structure ◽  
Earthquake Excitation ◽  
Accurate Identification ◽  
The Real ◽  
Response Characteristics

The accurate identification of the dynamic response characteristics of a building structure excited by input signals such as real earthquake or wind load is essential not only for the evaluation of the safety and serviceability of the building structure, but for the verification of an analytical model used in the seismic or wind design. In the field of system identification (SI) which constructs system matrices describing the accurate input/output relationship, it is critical that input should have enough energy to excite fundamental structural modes and a good quality of output containing structural information should be measured. In this study forced vibration testing which is important for correlating the mathematical model of a structure with the real one and for evaluating the performance of the real structure was implemented. There exist various techniques available for evaluating the seismic performance using dynamic and static measurements. In this paper, full scale forced vibration tests simulating earthquake response are implemented by using a hybrid mass damper. The finite element (FE) model of the structure was analytically constructed using ANSYS and the model was updated using the results experimentally measured by the forced vibration test. Pseudo-earthquake excitation tests showed that HMD induced floor responses coincided with the earthquake induced ones which was numerically calculated based on the updated FE model.

scholarly journals Dynamic Model Identification for 6-DOF Industrial Robots

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Li Ding ◽  
Hongtao Wu ◽  
Yu Yao ◽  
Yuxuan Yang
Keyword(s):  
Robot Manipulator ◽  
Industrial Robot ◽  
Model Identification ◽  
Friction Model ◽  
Industrial Robots ◽  
Parameters Identification ◽  
Identification Algorithm ◽  
Unknown Parameters ◽  
Joint Friction ◽  
Dynamical Parameters

A complete and systematic procedure for the dynamical parameters identification of industrial robot manipulator is presented. The system model of robot including joint friction model is linear with respect to the dynamical parameters. Identification experiments are carried out for a 6-degree-of-freedom (DOF) ER-16 robot. Relevant data is sampled while the robot is tracking optimal trajectories that excite the system. The artificial bee colony algorithm is introduced to estimate the unknown parameters. And we validate the dynamical model according to torque prediction accuracy. All the results are presented to demonstrate the efficiency of our proposed identification algorithm and the accuracy of the identified robot model.

scholarly journals Visible and real sizes of the COVID-19 pandemic in Ukraine

2021 ◽  
Author(s):  
Igor Nesteruk
Keyword(s):  
Mathematical Models ◽  
Real Number ◽  
Sir Model ◽  
Data Sets ◽  
Unknown Parameters ◽  
The Real ◽  
Epidemic Dynamics ◽  
Optimal Values

Abstract To simulate how the number of COVID-19 cases increases versus time, various data sets and different mathematical models can be used. In particular, previous simulations of the COVID-19 epidemic dynamics in Ukraine were based on smoothing of the dependence of the number of cases on time and the generalized SIR (susceptible-infected-removed) model. Since real number of cases is much higher than the official numbers of laboratory confirmed ones, there is a need to assess the degree of data incompleteness and correct the relevant forecasts. We have improved the method of estimating the unknown parameters of the generalized SIR model and calculated the optimal values ​​of the parameters. It turned out that the real number of diseases exceeded the officially registered values ​​by about 4.1 times at the end of 2020 in Ukraine. This fact requires a reassessment of the COVID-19 pandemic dynamics in other countries and clarification of world forecasts.

scholarly journals Efficient coding of natural scenes improves neural system identification

2022 ◽  
Author(s):  
Yongrong Qiu ◽  
David A Klindt ◽  
Klaudia P Szatko ◽  
Dominic Gonschorek ◽  
Larissa Hoefling ◽  
...  
Keyword(s):  
System Identification ◽  
Visual Processing ◽  
Neural System ◽  
Natural Scenes ◽  
Natural Environments ◽  
Retinal Neurons ◽  
Efficient Coding ◽  
Recorded Data ◽  
Identification Model ◽  
Regularized Model

Neural system identification aims at learning the response function of neurons to arbitrary stimuli using experimentally recorded data, but typically does not leverage coding principles such as efficient coding of natural environments. Visual systems, however, have evolved to efficiently process input from the natural environment. Here, we present a normative network regularization for system identification models by incorporating, as a regularizer, the efficient coding hypothesis, which states that neural response properties of sensory representations are strongly shaped by the need to preserve most of the stimulus information with limited resources. Using this approach, we explored if a system identification model can be improved by sharing its convolutional filters with those of an autoencoder which aims to efficiently encode natural stimuli. To this end, we built a hybrid model to predict the responses of retinal neurons to noise stimuli. This approach did not only yield a higher performance than the stand-alone system identification model, it also produced more biologically-plausible filters. We found these results to be consistent for retinal responses to different stimuli and across model architectures. Moreover, our normatively regularized model performed particularly well in predicting responses of direction-of-motion sensitive retinal neurons. In summary, our results support the hypothesis that efficiently encoding environmental inputs can improve system identification models of early visual processing.

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