Input Excitation Analysis for Black-Box Quadrotor Model System Identification

2020 ◽  
Author(s):  
John Angarita ◽  
Daniel Doyle ◽  
Gustavo Gargioni ◽  
Jonathan Black
Keyword(s):  
System Identification ◽  
Dynamic Models ◽  
Moving Average ◽  
Black Box ◽  
Autoregressive Moving Average ◽  
Nonlinear Responses ◽  
Box Models ◽  
Armax Model ◽  
Quadrotor System ◽  
Error Method

Abstract System identification provides a process to develop different dynamic models of varying structures based on user-defined requirements. For a quadrotor, system identification has been primarily in the field of off-white and grey-box models, but black-box models have the advantage of incorporating nonlinear aero-dynamic effects while also maintaining performance. For the identification, both a chirp and Hebert-Mackin parameter identification method waveform are used as inputs to maximize excitation while minimizing nonlinear responses. The quadrotor structure is defined by the an autoregressive with exogenous input (ARX) model, an autoregressive-moving-average (ARMAX) model, and a Box-Jenkins (BJ) models and then identified with the prediction error method. The black-box method shows that it maintains identification performance while improving upon the flexibility of different cases and ease of implementation.

scholarly journals Forecasting hydrologic parameters using linear and nonlinear stochastic models

2019 ◽  
Vol 11 (4) ◽  
pp. 1284-1301
Author(s):  
Hamed Nozari ◽  
Fateme Tavakoli
Keyword(s):  
Support Vector Machine ◽  
Moving Average ◽  
Sustainable Use ◽  
Rain Gauge ◽  
Support Vector ◽  
Autoregressive Moving Average ◽  
Monthly Precipitation ◽  
Precipitation Prediction ◽  
Armax Model ◽  
Monthly Discharge

Abstract One of the most important bases in the management of catchments and sustainable use of water resources is the prediction of hydrological parameters. In this study, support vector machine (SVM), support vector machine combined with wavelet transform (W-SVM), autoregressive moving average with exogenous variable (ARMAX) model, and autoregressive integrated moving average (ARIMA) models were used to predict monthly values of precipitation, discharge, and evaporation. For this purpose, the monthly time series of rain-gauge, hydrometric, and evaporation-gauge stations located in the catchment area of Hamedan during a 25-year period (1991–2015) were used. Out of this statistical period, 17 years (1991–2007), 4 years (2008–2011), and 4 years (2012–2015) were used for training, calibration, and validation of the models, respectively. The results showed that the ARIMA, SVM, ARMAX, and W-SVM ranked from first to fourth in the monthly precipitation prediction and SVM, ARIMA, ARMAX, and W-SVM were ranked from first to fourth in the monthly discharge and monthly evaporation prediction. It can be said that the SVM has fewer adjustable parameters than other models. Thus, the model is able to predict hydrological changes with greater ease and in less time, because of which it is preferred to other methods.

Closed-loop parametric identification of DC-DC converter

2018 ◽  
Vol 232 (10) ◽  
pp. 1429-1438 ◽  
Author(s):  
Subhransu Padhee ◽  
Umesh Chandra Pati ◽  
Kamalakanta Mahapatra
Keyword(s):  
System Identification ◽  
Closed Loop ◽  
Model Simulation ◽  
Moving Average ◽  
Parametric Identification ◽  
Buck Converter ◽  
Input Output ◽  
Closed Loop System ◽  
Auto Regressive ◽  
Error Method

This study provides a step-by-step analysis of closed-loop parametric system identification for DC-DC buck converter. In closed-loop parametric identification, input–output experimental data are used to estimate the transfer function coefficients of DC-DC buck converter. For system identification purpose, a high-frequency perturbation signal is injected in to the closed-loop system which acts as an input signal for identification experiment. Different input–output models such as Auto-Regressive eXogenous, Auto-Regressive Moving Average with eXogenous, output error, and Box–Jenkins are used to model the converter structure and prediction error method is used to estimate the parameters. Model validation schemes are used to validate the estimated model. Simulation and experimental analysis have been provided to validate the results obtained.

scholarly journals Predict the values of the time series using the (ARMAX) model with practical application

2016 ◽  
Vol 22 (88) ◽  
pp. 420
Author(s):  
فراس احمد محمد ◽  
مصطفى علي فخري
Keyword(s):  
Time Series ◽  
Black Box ◽  
Practical Application ◽  
Box Models ◽  
Armax Model ◽  
Black Box Models

        لقد اولى الباحثون اهتماماً كبيراً بدراسة نماذج الصندوق الاسود (black box models) وقد ركز هذا البحث في دراسة احد نماذج الصندوق الاسود وهو انموذج ARMAX الذي يعد مــن الــنماذج المهمة الــذي يـمــكـن الــحــصــول مــن خـــلالــه عـــلــى عـــــدد من الحــــالات الخــاصة وهي نماذج (AR , MA ARMA , ARX) والذي يدمج بين اسلوب السلاسل الزمنية التي تعتمد على البيانات التاريخية واسلوب الانحدار بمتغيرات توضيحية فضلاً عن ذلك الاخطاء السابقة , وقد ظهرت اهمية انموذج ARMAX في الكثير من المجالات التطبيقية ذات تماس مباشر بحياتنا اليومية , وتتالف عملية بناء انموذج ARMAX من عدة مراحل تقليدية وهي التشخيص أذ تم تشخيص رتبة الانموذج باستخدام عدد من المعايير وهي معيار خطأ التنبؤ النهائي (FPE) ومعيار معلومات أكاكي (AIC) والتقدير باستخدام طريقة المربعات الصغرى التكرارية باستخدام عامل التغاضي (RLS – F) وطريقة الانحدار الخطي الزائف التكرارية (RPLR) والتي جاءت في المرتبة الاولى وطريقة (RLS – F) جاءت في المرتبة الثانية  وتأتي اخيراً عملية التنبؤ ب(30) قيمة لدرجة الحرارة العظمى اليومية اعتماداً على سرعة الرياح اليومية .  

scholarly journals EXPERIMENTAL IDENTIFICATION OF AN ELASTO-MECHANICAL MULTI-DEGREE-OF-FREEDOM-SYSTEM USING STOCHASTIC SIGNALS

2016 ◽  
Vol 7 ◽  
pp. 64 ◽  
Author(s):  
Simon Schleiter ◽  
Okyay Altay ◽  
Sven Klinkel
Keyword(s):  
System Identification ◽  
Civil Engineering ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Engineering Structures ◽  
Stochastic Signals ◽  
Identification Methods ◽  
Recent Developments ◽  
Rc Building ◽  
Frequency Domain Decomposition

The determination of dynamic parameters are the central points of the system identification of civil engineering structures under dynamic loading. This paper first gives a brief summary of the recent developments of the system identification methods in civil engineering and describes mathematical models, which enable the identification of the necessary parameters using only stochastic input signals. Relevant methods for this identification use Frequency Domain Decomposition (FDD), Autoregressive Moving Average Models (ARMA) and the Autoregressive Models with eXogenous input (ARX). In a first step an elasto-mechanical mdof-system is numerically modeled using FEM and afterwards tested numerically by above mentioned identification methods using stochastic signals. During the second campaign, dynamic measurements are conducted experimentally on a real 7-story RC-building with ambient signal input using sensors. The results are successfully for the relevant system identification methods.

Recursive Identification of the Dynamic Behavior in a Distillation Column by Means of Autoregressive Models

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Lakhdar Aggoune ◽  
Yahya Chetouani ◽  
Hammoud Radjeai
Keyword(s):  
Performance Measures ◽  
Distillation Column ◽  
Moving Average ◽  
Information Criterion ◽  
Recursive Identification ◽  
Autoregressive Moving Average ◽  
Model Parameters ◽  
Recursive Algorithms ◽  
Optimal Model ◽  
Armax Model

In this study, an Autoregressive with eXogenous input (ARX) model and an Autoregressive Moving Average with eXogenous input (ARMAX) model are developed to predict the overhead temperature of a distillation column. The model parameters are estimated using the recursive algorithms. In order to select an optimal model for the process, different performance measures, such as Aikeke's Information Criterion (AIC), Root Mean Square Error (RMSE), and Nash–Sutcliffe Efficiency (NSE), are calculated.

Fast Diagnosis of Volatile Organic Compounds with a Temperature-Modulated Chemoresistor

2011 ◽  
Vol 495 ◽  
pp. 310-313 ◽  
Author(s):  
Amir Amini ◽  
Seyed Mohsen Hosseini-Golgoo
Keyword(s):  
Cross Validation ◽  
Operating Temperature ◽  
Moving Average ◽  
Feature Space ◽  
Autoregressive Moving Average ◽  
Temperature Modulation ◽  
Voltage Waveform ◽  
Volatile Organic ◽  
Gas Identification ◽  
Armax Model

Virtual arrays formed by operating temperature modulation of a commercial non selective chemoresistor have been utilized for gas identification. Here, we are reporting the details of a refined system which distinctly classifies methanol, ethanol, 1-butanol, acetone and hydrogen contaminations in a wide concentration range. A staircase voltage waveform of 5 plateaus is applied to the sensor’s microheater and gas recognition is achieved in 25 s. Sensor’s output is modeled by an “autoregressive moving average with exogenous variables” (ARMAX) model. The modeling parameters obtained for an unknown analyte are utilized as the components of its feature vectors which afford its classification in a feature space. Cross-validation in the 5 to 100 ppm concentration range for H2, and 200 to 2000 ppm for the other analytes examined, resulted in an overall classification success rate of 100%.

Assessing the Effects of Terrorism on Tourism by Use of Time Series Methods

2003 ◽  
Vol 9 (2) ◽  
pp. 179-190 ◽  
Author(s):  
Brian W. Sloboda
Keyword(s):  
Time Series ◽  
Impact Analysis ◽  
Moving Average ◽  
Single Equation ◽  
Autoregressive Moving Average ◽  
Short Term ◽  
Explanatory Variables ◽  
Use Of Time ◽  
Dependent Variables ◽  
Armax Model

This paper presents an assessment of the effects of terrorism on tourism by using time series methods, namely the ARMAX (autoregressive moving average with explanatory variables) model. This is a single-equation approach, which has the ability to provide impact analysis easily. The use of the ARMAX model allows for the general shape of the lag distribution of the impacts of the explanatory variables based on the ratio of lag polynomials for the independent and dependent variables. The ARMAX models, like the ARIMA models, provide for a short-term assessment of terrorist incidents on tourism.

System Identification of Small Loudspeakers Using ARMA Model

2010 ◽  
Author(s):  
Jaewon Choi ◽  
Mohsen Nakhaeinejad ◽  
Michael D. Bryant
Keyword(s):  
System Identification ◽  
Electrical Impedance ◽  
Moving Average ◽  
Arma Model ◽  
Data Driven ◽  
Autoregressive Moving Average ◽  
Autoregressive Moving Average Model ◽  
Driven System ◽  
Moving Average Model ◽  
Arma Modeling

This study illustrates a data driven system identification method for loudspeaker model estimation using the knowledge of the underlying physics of loudspeakers. In this study, diaphragm displacement is analyzed to estimate the model structure and parameters based on impulse response equivalent sampling and autoregressive moving average model. The estimated loudspeaker models are compared in the frequency response function plot. It is shown that the autoregressive moving average (ARMA) based loudspeaker models are comparable to the model estimated by the conventional method based on electrical impedance. Also ARMA modeling strategies with and without knowledge of the physics-based model are compared. Some issues related to ARMA modeling are addressed.

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