ARMAV Techniques for Traffic Excited Bridges

1998 ◽  
Vol 120 (3) ◽  
pp. 713-718 ◽  
Author(s):  
L. Garibaldi ◽  
E. Giorcelli ◽  
B. A. D. Piombo
Keyword(s):  
System Identification ◽  
Time Window ◽  
Moving Average ◽  
System Response ◽  
Model Parameters ◽  
Sampled Data ◽  
Discrete Time System ◽  
Traffic Conditions ◽  
Multiple Input ◽  
Auto Regressive

In this paper ARMAV (Auto Regressive Moving Average Vector) models are used for system identification and modal analysis purposes. This time domain technique allows to estimate a discrete time system response function without performing any domain change (i.e. it doesn’t use FFT and IFFT to evaluate the model parameters) and without applying any time window (also when sampled data are non periodic): this leads to well-estimated system parameters, also for short data records. These models are useful to perform system identification for multiple input-output cases also when the excitation is just statistically known. The present analysis is dedicated to a scaled bridge, designed according to the theory of models, whose static and dynamic characteristics are compatible to those of real bridges. The aim of the tests is to collect a series of supervised measurements in a controlled environment, with statistically defined traffic conditions; the comparison of the model results with those acquired on the real bridge is the compulsory step towards a correct modelling of bridges for their identification and monitoring. The paper reports encouraging results obtained with experimental simulations on the model.

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.

ADAPTIVE, FREQUENCY DOMAIN, 2-D MODELING USING SPATIOTEMPORAL SIGNALS

1996 ◽  
Vol 06 (04) ◽  
pp. 351-358
Author(s):  
WASFY B. MIKHAEL ◽  
HAOPING YU
Keyword(s):  
Frequency Domain ◽  
Moving Average ◽  
System Modeling ◽  
Arma Model ◽  
Model Parameters ◽  
Two Dimensional ◽  
Convergence Factor ◽  
Arma Process ◽  
Auto Regressive ◽  
Unknown System

In this paper, an adaptive, frequency domain, steepest descent algorithm for two-dimensional (2-D) system modeling is presented. Based on the equation error model, the algorithm, which characterizes the 2-D spatially linear and invariant unknown system by a 2-D auto-regressive, moving-average (ARMA) process, is derived and implemented in the 3-D spatiotemporal domain. At each iteration, corresponding to a given pair of input and output 2-D signals, the algorithm is formulated to minimize the error-function’s energy in the frequency domain by adjusting the 2-D ARMA model parameters. A signal dependent, optimal convergence factor, referred to as the homogeneous convergence factor, is developed. It is the same for all the coefficients but is updated once per iteration. The resulting algorithm is called the Two-Dimensional, Frequency Domain, with Homogeneous µ*, Adaptive Algorithm (2D-FD-HAA). In addition, the algorithm is implemented using the 2-D Fast Fourier Transform (FFT) to enhance the computational efficiency. Computer simulations demonstrate the algorithm’s excellent adaptation accuracy and convergence speed. For illustration, the proposed algorithm is successfully applied to modeling a time varying 2-D system.

Modeling and Identification of a Nonlinear SDOF Moored Structure, Part 2—Comparisons and Sensitivity Study

2004 ◽  
Vol 126 (2) ◽  
pp. 183-190 ◽  
Author(s):  
S.C.S. Yim ◽  
S. Narayanan
Keyword(s):  
System Identification ◽  
Small Body ◽  
Ocean Waves ◽  
System Response ◽  
Mooring System ◽  
Nonlinear Structure ◽  
Single Output ◽  
Submerged Sphere ◽  
Multiple Input ◽  
Identification Technique

A system-identification technique based on the Reverse Multiple-Input/Single-Output (R-MI/SO) procedure is applied to identify the parameters of an experimental mooring system exhibiting nonlinear behavior. In Part 1, two nonlinear small-body hydrodynamic Morison type formulations: (A) with a relative-velocity (RV) model, and (B) with an independent-flow-field (IFF) model, are formulated. Their associated nonlinear system-identification algorithms based on the R-MI/SO system-identification technique: (A.1) nonlinear-structure linearly damped, and (A.2) nonlinear-structure coupled hydrodynamically damped for the RV model, and (B.1) nonlinear-structure nonlinearly damped for the IFF model, are developed for an experimental submerged-sphere nonlinear mooring system under ocean waves. The analytic models and the associated algorithms for parametric identification are described. In this companion paper (Part 2), we use the experimentally measured input wave and output system response data and apply the algorithms derived based on the multiple-input/single-output linear analysis of the reverse dynamic systems to identify the system parameters. The two nonlinear models are examined in detail and the most suitable physical representative model is selected for the mooring system considered. A sensitive analysis is conducted to investigate the coupled hydrodynamic forces modeled by the Morison equation, the nonlinear stiffness from mooring lines and the nonlinear response. The appropriateness of each model is discussed in detail.

Heuristic Order Reduction of NARX-OBF models Applied to Nonlinear System Identification

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Elder Oroski ◽  
Beatriz Do Santos Pês ◽  
Adolfo Bauchspiess ◽  
Marco Egito Coelho
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Model Performance ◽  
Order Reduction ◽  
Computational Effort ◽  
Model Parameters ◽  
Auto Regressive ◽  
Narx Models

Nonlinear system identification concerns the determination of the model structure and its parameters. Although the designers often seek the best model for each system, it can be tricky to determine, at the same time, the best structure and the parameters which optimize the model performance. This paper proposes the use of a Genetic Algorithm, GA, and the Levenberg-Marquardt, LM, method to obtain the model parameters, as well as perform the order reduction of the model. In order to validate the proposed methodology, the identification of a magnetic levitator, operating in closed loop, was performed. The class NARX-OBF, Nonlinear Auto Regressive with eXogenous input-Orthonormal Basis Function, was used. The use of OBF functions aims to reduce the number of terms in NARX models. Once the model is found, the order reduction is performed using GA and LM, in a hybrid application, capable of determining the model parameters and reducing the original model order, simultaneously. The results show, considering the inherent trade-of between accuracy and computational effort, the proposed methodology provided an implementation with good mean square error, when compared with the full NARX-OBF model.

Dynamic Performance of Flow Control Valve Using Different Models of System Identification

2010 ◽  
Vol 437 ◽  
pp. 393-396
Author(s):  
Ho Chang
Keyword(s):  
System Identification ◽  
Flow Control ◽  
Dynamic Characteristics ◽  
Moving Average ◽  
Damping Ratio ◽  
Dynamic Performance ◽  
Control Valve ◽  
Flow Control Valve ◽  
Auto Regressive ◽  
Input Model

This study measures the dynamic characteristics of flow control valve by a self-developed square pressure wave generator (SPWG). Comprised of a revolving shaft and a fixed ring, SPWG generates square pressure waves by the differential function of rotation between these two critical components. With the highly sensitive piezoelectric pressure sensor as the reference sensor, tests are conducted concurrently using a flow control valve. Under the same experimental parameters, the dynamic characteristics of flow control valve are evaluated by four kinds of system identification methods, namely ARX (Auto-Regressive with eXogenous input model), ARMAX (Auto-Regressive moving Average with eXogenous input model), OE (Output Error model) and BJ (Box-Jenkins model). The experimental results indicate that the dynamic performance of the tested flow control valve for resonance frequency, resonance peak and damping ratio are 1565.6 Hz, 0.9753 db and 0.4044, respectively.

A Heat Transfer System Identification Method for Shape Memory Alloy Wires

2017 ◽  
Author(s):  
Cody Wright ◽  
Onur Bilgen
Keyword(s):  
Heat Transfer ◽  
System Identification ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Constitutive Models ◽  
Heat Transfer Model ◽  
System Response ◽  
Model Parameters ◽  
Transfer Model ◽  
Mechanical Coupling

The thermo-mechanical coupling of shape memory alloys has been modeled comprehensively using energy based constitutive models. These constitutive models describe the relationship of temperature, stress and strain in material and propose a solid methodology for system identification of model parameters. Equally important in the dynamics of shape memory alloy applications is the heat transfer model. Heat transfer models have been proposed but a complete resource for system identification of the model parameters is missing in the literature. Therefore, in this paper, the parameters for a low-order heat transfer model are identified experimentally. It is shown that for all parameters the measured parameters accurately model the system leading to the necessary values for use in predictive models. Furthermore, it is shown that using nominal values will produce inaccuracies in the predicted system response.

Prediction of Network Utilization Based on ARMA Model and RELS

2014 ◽  
Vol 945-949 ◽  
pp. 2780-2783 ◽  
Author(s):  
Hui Zhang ◽  
Fang He ◽  
Chun Yan Han
Keyword(s):  
Moving Average ◽  
Arma Model ◽  
Estimation Algorithm ◽  
Networked Control System ◽  
Model Parameters ◽  
Predictive Algorithm ◽  
Network Utilization ◽  
Parameter Estimation Algorithm ◽  
General Network ◽  
Auto Regressive

This paper focused on predictive algorithm of network utilization for networked control system (NCS). Auto-Regressive and Moving Average (ARMA) model was presented for general network utilization, which with fixed constant and known white noise. ARMA model parameters are estimated using parameter estimation algorithm of Recursive Extended Least Squares (RELS). Finally, a simulation example was given to realize RELS of ARMA model. Predictive output of network utilization can be obtained and converge to real state.

Prognosticating the spread of covid-19 pandemic based on optimal arima estimators

2020 ◽  
Vol 20 ◽  
Author(s):  
Venuka Sandhir ◽  
Vinod Kumar ◽  
Vikash Kumar
Keyword(s):  
South Africa ◽  
Statistical Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Stable Case ◽  
Johns Hopkins University ◽  
Modelling Techniques ◽  
Auto Regressive ◽  
Global Threat

Background: COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19from the explicit data based on optimal ARIMA model estimators. Methods: Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. Results: The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to number of autoregressive terms, d refers to number of times the series has to be differenced before it becomes stationary, and q refers to number of moving average terms. Results obtained from ARIMA model showed significant decrease cases in Australia; stable case for China and rising cases has been observed in other countries. Conclusion: This study tried their best at predicting the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

scholarly journals Analysis of Cooperative Perception in Ant Traffic and Its Effects on Transportation System by Using a Congestion-Free Ant-Trail Model

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2393
Author(s):  
Prafull Kasture ◽  
Hidekazu Nishimura
Keyword(s):  
Average Velocity ◽  
Transportation System ◽  
Self Organization ◽  
Model Parameters ◽  
Agent Based ◽  
Model Simulations ◽  
Mathematical Representations ◽  
Traffic Conditions ◽  
Traffic Jams ◽  
Ant Trail

We investigated agent-based model simulations that mimic an ant transportation system to analyze the cooperative perception and communication in the system. On a trail, ants use cooperative perception through chemotaxis to maintain a constant average velocity irrespective of their density, thereby avoiding traffic jams. Using model simulations and approximate mathematical representations, we analyzed various aspects of the communication system and their effects on cooperative perception in ant traffic. Based on the analysis, insights about the cooperative perception of ants which facilitate decentralized self-organization is presented. We also present values of communication-parameters in ant traffic, where the system conveys traffic conditions to individual ants, which ants use to self-organize and avoid traffic-jams. The mathematical analysis also verifies our findings and provides a better understanding of various model parameters leading to model improvements.

An MEWMA-based segmental multivariate hidden Markov model for degradation assessment and prediction

2021 ◽  
pp. 1748006X2199052
Author(s):  
Yaping Li ◽  
Enrico Zio ◽  
Ershun Pan
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
Average Run Length ◽  
Moving Average ◽  
Model Parameters ◽  
Current Time ◽  
Multivariate Processes ◽  
Industrial Systems ◽  
Hidden States ◽  
Multivariate Signals

Degradation is an unavoidable phenomenon in industrial systems. Hidden Markov models (HMMs) have been used for degradation modeling. In particular, segmental HMMs have been developed to model the explicit relationship between degradation signals and hidden states. However, existing segmental HMMs deal only with univariate cases, whereas in real systems, signals from various sensors are collected simultaneously, which makes it necessary to adapt the segmental HMMs to deal with multivariate processes. Also, to make full use of the information from the sensors, it is important to differentiate stable signals from deteriorating ones, but there is no good way for this, especially in multivariate processes. In this paper, the multivariate exponentially weighted moving average (MEWMA) control chart is employed to identify deteriorating multivariate signals. Specifically, the MEWMA statistic is used as a comprehensive indicator for differentiating multivariate observations. Likelihood Maximization is used to estimate the model parameters. To avoid underflow, the forward and backward probabilities are normalized. In order to assess degradation, joint probabilities are defined and derived. Further, the occurrence probability of each degradation state at the current time, as well as in the future, is derived. The Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset of NASA is employed for comparative analysis. In terms of degradation assessment and prediction, the proposed model performs very well in general. By sensitivity analysis, we show that in order to improve further the performance of the method, the weight of the chart should be set relatively small, whereas the method is not sensitive to the change of the in-control average run length (ARL).

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