scholarly journals Perancangan Sistem Pengendalian Menggunakan Kontroler PID pada Gerakan Pitch dan Roll untuk Stabilitas Attitude Hexacopter

2021 ◽  
Vol 11 (2) ◽  
pp. 386-398
Author(s):  
Achmad Ryan Perdana ◽  
Illa Rizianiza
Keyword(s):  
Transfer Function ◽  
Moving Average ◽  
Black Box ◽  
Data Input ◽  
Rotary Wing

Drone Hexacopter merupakan salah satu jenis UAV tipe rotary wing. Hexacopter memiliki 6 buah motor penggerak, biasa disebut multirotor. Drone Hexacopter memiliki sistem yang kompleks dan nonlinear. Sifat sistem hexacopter menyebabkan proses pengendalian sulit dilakukan saat sistem kontrol tidak dirancang dengan baik sehingga menjadi tantangan untuk penelitian mendatang. Dibutuhkan perancangan sistem kontrol hexacopter yang dapat menjaga kestabilan gerak attitude saat drone berada di udara yaitu variabel pitch dan roll. Tujuan dari penelitian ini ialah menentukan parameter PID untuk gerakan pitch dan roll pada hexacopter. Penelitian ini, pemodelan sistem untuk mendapatkan transfer function dilakukan dengan engineering tools black-box modeling yaitu Autoreggresive Moving Average eXogenous (ARMAX). Metode ini menggunakan data input dan output drone berupa sudut perintah dan sudut aktual (pitch dan roll). Perancangan kontroler dilakukan dengan mencari parameter PID lalu dilakukan simulasi menggunakan software komputer. Hasil setelah perancangan diharapkan mengalami improvisasi atau lebih baik dari sistem sebelumnya setelah dilakukan simulasi.

Random filters which preserve the stability of random inputs

1988 ◽  
Vol 20 (2) ◽  
pp. 275-294 ◽  
Author(s):  
Stamatis Cambanis
Keyword(s):  
Transfer Function ◽  
Moving Average ◽  
Random Processes ◽  
Time Shift ◽  
Linear Filter ◽  
Random Inputs ◽  
The Stability ◽  
Non Gaussian ◽  
Random Gain

A stationary stable random processes goes through an independently distributed random linear filter. It is shown that when the input is Gaussian or harmonizable stable, then the output is also stable provided the filter&s transfer function has non-random gain. In contrast, when the input is a non-Gaussian stable moving average, then the output is stable provided the filter&s randomness is due only to a random global sign and time shift.

Modal Analysis of Machine Tool Structures Based on Experimental Data

1983 ◽  
Vol 105 (4) ◽  
pp. 282-287 ◽  
Author(s):  
K. F. Eman ◽  
K. J. Kim
Keyword(s):  
Transfer Function ◽  
Machine Tool ◽  
Modal Analysis ◽  
Moving Average ◽  
Parametric Representation ◽  
Modeling Procedure ◽  
Fitting Procedure ◽  
Machine Tool Structures ◽  
Computer Based ◽  
Three Degree Of Freedom

The basic problem in modal analysis of machine tool structures is the extraction of modal parameters from the measured transfer function data. Conventionally this task is performed in two steps. The transfer function is determined using a Digital Fourier Analyzer followed by a suitable curve fitting procedure. In order to avoid the inherent problems associated with these procedures a new approach for modal analysis is proposed in this paper. Anticipating the stochastic nature of the systems excitation and response Modified Autoregressive Moving Average Vector models (MARMAV) are proposed. The modeling procedure yields a parametric representation of the structural behavior allowing the extraction of the modal information in one step, directly, rather than in two as in the conventional approaches. The mathematical foundation for the approach is given along with its application to a simulated three-degree-of-freedom system and a knee type milling machine. The newly proposed procedure is commensurate to the existing ones in light of the computational efforts involved; however, it eliminates the subjective judgment of the analyst since the modeling procedure is based on rigorous statistical adequacy checks. Finally, the proposed approach is amenable for implementation in a computer-based machine tool structural dynamics analyzer.

scholarly journals Transfer Function Analysis: Modelling Residential Building Costs in New Zealand by Including the Influences of House Price and Work Volume

Buildings ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 152
Author(s):  
Linlin Zhao ◽  
Jasper Mbachu ◽  
Zhansheng Liu ◽  
Huirong Zhang
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Transfer Function ◽  
Moving Average ◽  
Cost Data ◽  
Autoregressive Integrated Moving Average ◽  
Work Volume ◽  
Moving Average Models ◽  
Function Models ◽  
Transfer Function Models

An accurate cost estimate not only plays a key role in project feasibility studies but also in achieving a final successful outcome. Conventionally, estimating cost typically relies on the experience of professionals and cost data from previous projects. However, this process is complex and time-consuming, and it is challenging to ensure the accuracy of the estimates. In this study, the bivariate and multivariate transfer function models were adopted to estimate and forecast the building costs of two types of residential buildings in New Zealand: Low-rise buildings and high-rise buildings. The transfer function method takes advantage of the merits of univariate time series analysis and the power of explanatory variables. In the dynamic project conduction environment, simply including building cost data in the cost forecasting models is not valid for making predictions, because the change in demand must be considered. Thus, the time series of house prices and work volume were used to explain exogenous effects in the transfer function model. To demonstrate the effectiveness of transfer function models, this study compared the results generated by the transfer function models with autoregressive integrated moving average models. According to the forecasting performance of the models, the proposed approach achieved better results than autoregressive integrated moving average models. The proposed method can provide accurate cost estimates that can help stakeholders in project budget planning and management strategy making at the early stage of a project.

scholarly journals Aplikasi Jaringan Saraf Tiruan dengan Metode Fungsi Tan-Sigmoid dalam Memprediksi Kinerja Struktur Bangunan Gedung

2021 ◽  
Vol 9 (1) ◽  
pp. 34-40
Author(s):  
Muhammad Gala Garcya ◽  
Zulfikar Djauhari ◽  
Reni Suryanita
Keyword(s):  
Finite Element ◽  
Transfer Function ◽  
Mean Square Error ◽  
Displacement Velocity ◽  
Mean Square ◽  
Data Input ◽  
Structural Engineer ◽  
Fema 356

Gempa bumi merupakan salah satu ancaman terbesar terhadap gedung, sehingga perlu untuk mendesain gedung dengan memperhitungkan pembebanan gempa bumi yang terjadi. Dengan bantuan software finite element dapat diperoleh respons struktur berupa displacement, velocity, dan acceleration yang terjadi akibat gempa bumi. Jaringan Saraf Tiruan (JST) merupakan salah satu metode yang dapat memprediksi kerusakan bangunan dengan memanfaatkan data respons struktur dengan waktu analisis yang relatif lebih singkat dibandingkan menganalisis struktur satu per satu. Penelitian ini bertujuan untuk menganalisis data gempa dengan magnitude intensitas tinggi yang berbeda-beda. Data input dan output diperoleh melalui software Finite Element untuk menghasilkan jumlah data yang diperlukan JST yaitu sebanyak 4489 data. Pada penelitian ini, komposisi yang digunakan untuk training, testing, dan validating adalah 60%, 25%, dan 15% masing – masingnya. Data input yang digunakan yaitu waktu, acceleration arah x dan y, velocity arah x dan y, serta displacement arah x dan y. Sedangkan untuk data target yang digunakan yaitu kinerja struktur yang ditentukan oleh FEMA 356 dan simpangan antar lantai arah x dan y. Hasil pengujian menunjukkan analisis oleh JST yang menggunakan transfer function Tan-Sigmoid menunjukkan nilai R2 sebesar 97,542% dan Mean Square Error (MSE) yang dihasilkan yaitu sebesar 1,2449.E-07. Hal ini menunjukkan analisis JST dengan transfer function Tan-Sigmoid dapat digunakan untuk memprediksi kinerja dari struktur dengan cepat dan akurat. Dengan demikian metode ini diharapkan dapat direkomendasikan untuk Structural Engineer dan perencana gedung dalam mendesain bangunan gedung bertingkat tahan gempa.

scholarly journals Predicting Lower Band Chorus With Autoregressive‐Moving Average Transfer Function (ARMAX) Models

2019 ◽  
Vol 124 (7) ◽  
pp. 5692-5708
Author(s):  
Laura E. Simms ◽  
Mark J. Engebretson ◽  
Craig J. Rodger ◽  
Jesper W. Gjerloev ◽  
Geoffrey D. Reeves
Keyword(s):  
Transfer Function ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Lower Band

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.

Random filters which preserve the stability of random inputs

1988 ◽  
Vol 20 (02) ◽  
pp. 275-294
Author(s):  
Stamatis Cambanis
Keyword(s):  
Transfer Function ◽  
Moving Average ◽  
Random Processes ◽  
Time Shift ◽  
Linear Filter ◽  
Random Inputs ◽  
The Stability ◽  
Non Gaussian ◽  
Random Gain

A stationary stable random processes goes through an independently distributed random linear filter. It is shown that when the input is Gaussian or harmonizable stable, then the output is also stable provided the filter&s transfer function has non-random gain. In contrast, when the input is a non-Gaussian stable moving average, then the output is stable provided the filter&s randomness is due only to a random global sign and time shift.

On the Efficiency Between Forecasting Models—A Study in the Indian Context

1987 ◽  
Vol 36 (1-2) ◽  
pp. 19-28
Author(s):  
Lakshmikanta Datta
Keyword(s):  
Transfer Function ◽  
Moving Average ◽  
Arima Model ◽  
Consumer Price Index ◽  
Transfer Function Model ◽  
Industrial Workers ◽  
Economic Time Series ◽  
Function Model ◽  
Forecasting Models ◽  
Economic Time

In this paper, we have investigated the relative performances of two types of forecasting models, namely univariate autoregressive integrated moving average (ARIMA) model and transfer function model, with the help of two Indian economic time series viz. (i) Money Supply (M3 ) and (ii) Consumer Price Index Numbers for Industrial Workers. Our emperical results show that the efficiency of transfer function model is substantially superior to that of the univariate model.

BLACK BOX MODELLING THE THERMAL BEHAVIOUR OF IHOUSE USING AUTO REGRESSIVE AND MOVING AVERAGE (ARMA) MODEL

2016 ◽  
Vol 78 (6-13) ◽  
Author(s):  
Shamsul Faisal Mohd Hussein ◽  
Hoaison Nguyen ◽  
Shahrum Shah Abdullah ◽  
Yuto Lim ◽  
Yasuo Tan
Keyword(s):  
Control System ◽  
Simulation Model ◽  
Thermal Behaviour ◽  
Moving Average ◽  
Arma Model ◽  
Black Box ◽  
Modelling Method ◽  
Auto Regressive ◽  
Recorded Data ◽  
Better Than

Modelling and simulation of the dynamic thermal behaviour of a building is important to test any proposed thermal comfort control system and strategy in the building. A simulation model can be obtained by using either the white box, grey box or black box modelling method. This research focuses on the usage of auto regressive and moving average (ARMA) model, a type of black box model that represents the dynamic thermal behaviour of iHouse testbed and uses real recorded data from the testbed and limited knowledge regarding the physical characteristics of the testbed. The performance of the ARMA model developed in this research is compared with the performance of House Thermal Simulator, a previously developed model, based on grey box modelling. Results obtained shows that ARMA model works better than House Thermal Simulator in some aspects.  

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