Parametric Identification of Vehicle’s Vertical Dynamics Using Vector Autoregressive Moving Average Models

2006 ◽  
Author(s):  
Dimitris V. Koulocheris ◽  
Vasilis K. Dertimanis
Keyword(s):  
Ride Comfort ◽  
Moving Average ◽  
Estimation Procedure ◽  
Parametric Identification ◽  
Practical Implementation ◽  
Vertical Acceleration ◽  
Vector Autoregressive ◽  
Control Units ◽  
Conflicting Objectives ◽  
The Stability

The assessment of vertical dynamics in modern ground vehicles is a difficult task with crucial importance, as it appears to be possessed by a number of conflicting objectives, such as ride comfort and stability. Thus, the effective use of possible control units is depended by the successful description of the vertical performance. The aim of this study is to provide a closed description of vehicles’ vertical dynamics using VARMA models, which are estimated by means of a novel, hybrid optimization algorithm and a corresponding estimation procedure. The hybrid algorithm interconnects the diverse characteristics of its deterministic and stochastic counterparts, while the estimation procedure assures the stability and invertibility requirements in the resulted models. For the practical implementation of the above, a five dimensional VARMA model is used for the description of a passenger vehicle, through the acquisition of noise–corrupted vertical acceleration measurements.

A Fuzzy Logic Controller for Vehicle-Active Suspension Systems

2013 ◽  
Vol 805-806 ◽  
pp. 1645-1649 ◽  
Author(s):  
Yun Quan Sun ◽  
Li Feng Zhao ◽  
Wei Xiang
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Ride Comfort ◽  
Active Suspension ◽  
Suspension System ◽  
Vertical Acceleration ◽  
Suspension Systems ◽  
System A ◽  
Active Suspension Systems ◽  
The Stability

This paper propose the study of automobile active suspension system for the purpose of improving ride comfort to passengers and simultaneously improving the stability of vehicle by reducing vibration effects on suspension system. A fuzzy-logic-based control for vehicle-active suspension is suggested. The vehicle vibration and disturbance are reduced considerably with a fuzzy logic controller, to enhance comfort in riding faced with uncertain road terrains. A quarter-car active suspension system is controlled to reduce the vertical acceleration, suspension stroke and tire deflection. Simulation studies clearly demonstrate the effectiveness of the fuzzy logic controller for active suspension systems. The performance of the fuzzy logic controller under variations in the suspension component characteristics are also studied and are found to give reasonably good responses.

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

Multivariate Markov-switching score-driven models: an application to the global crude oil market

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Szabolcs Blazsek ◽  
Alvaro Escribano ◽  
Adrian Licht
Keyword(s):  
Crude Oil ◽  
Moving Average ◽  
Local Level ◽  
Markov Switching ◽  
Impulse Response Functions ◽  
Common Trends ◽  
Vector Autoregressive ◽  
Oil Market ◽  
Long Run ◽  
Special Cases

Abstract A new class of multivariate nonlinear quasi-vector autoregressive (QVAR) models is introduced. It is a Markov switching score-driven model with stochastic seasonality for the multivariate t-distribution (MS-Seasonal-t-QVAR). As an extension, we allow for the possibility of having common-trends and nonlinear co-integration. Score-driven nonlinear updates of local level and seasonality are used, which are robust to outliers within each regime. We show that VAR integrated moving average (VARIMA) type filters are special cases of QVAR filters. Using exclusion, sign, and elasticity identification restrictions in MS-Seasonal-t-QVAR with common-trends, we provide short-run and long-run impulse response functions for the global crude oil market.

scholarly journals Choosing between identification schemes in noisy-news models

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Joshua C. C. Chan ◽  
Eric Eisenstat ◽  
Gary Koop
Keyword(s):  
Bayesian Methods ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Structural Shocks ◽  
Vector Autoregressive ◽  
Identification Scheme ◽  
Identification Schemes ◽  
The One ◽  
Small Models ◽  
Structural Vector

AbstractThis paper is about identifying structural shocks in noisy-news models using structural vector autoregressive moving average (SVARMA) models. We develop a new identification scheme and efficient Bayesian methods for estimating the resulting SVARMA. We discuss how our identification scheme differs from the one which is used in existing theoretical and empirical models. Our main contributions lie in the development of methods for choosing between identification schemes. We estimate specifications with up to 20 variables using US macroeconomic data. We find that our identification scheme is preferred by the data, particularly as the size of the system is increased and that noise shocks generally play a negligible role. However, small models may overstate the importance of noise shocks.

Application of Practical Observer to Semi-Active Suspensions

1998 ◽  
Vol 122 (2) ◽  
pp. 284-289 ◽  
Author(s):  
H. Nakai ◽  
S. Oosaku ◽  
Y. Motozono
Keyword(s):  
Ride Comfort ◽  
Difficult Problem ◽  
Nonlinear Observer ◽  
Vertical Acceleration ◽  
Acceleration Sensor ◽  
Active Suspensions ◽  
Linear Observer ◽  
Gain Scheduled

This paper presents the development of gain-scheduled observers for semi-active suspensions. The states of the semi-active suspensions must be accurately obtained because the accuracy directly affects system performances such as ride comfort. Nonlinearity in the absorber of the semi-active suspensions is a difficult problem for estimating the accurate states using conventional linear observer theories. To solve this problem, we have designed a new gain-scheduled observer by introducing two improvements. The validity of this nonlinear observer was confirmed by simulations and experiments. The results indicate that the present observer can accurately estimate the suspension stroke velocity using the vertical acceleration sensor on the sprung mass. [S0022-0434(00)02302-9]

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.

FORECASTING OF OIL AND AGRICULTURAL COMMODITY PRICES: VARMA VERSUS ARMA

2017 ◽  
Vol 12 (03) ◽  
pp. 1750012 ◽  
Author(s):  
MUSTAFA GÜLERCE ◽  
GAZANFER ÜNAL
Keyword(s):  
Moving Average ◽  
Commodity Prices ◽  
Wavelet Coherence ◽  
Autoregressive Moving Average ◽  
Multiple Time ◽  
Energy Prices ◽  
Agricultural Commodity ◽  
Vector Autoregressive ◽  
Dynamic Correlations ◽  
Model Free

The aim of this paper is to show that the estimates made with vector autoregressive–moving-average (ARMA) models based on the coherent time intervals of the multiple time series give more precise results than the univariate case. The previous literature on dynamic correlations (co-movement) in between food and energy prices has mixed results and mainly based on parametric approaches. Therefore, partial wavelet coherence (PWC) and multiple wavelet coherence (MWC) methods are used, respectively, to uncover the coherency simultaneously for time and frequency domains. In our study; world oil, corn, soybeans, wheat and sugar prices are examined instead of the return and volatility relationship between oil and agricultural commodities due to model-free approach of wavelet analysis.

scholarly journals A Set of State–Space Models at a High Disaggregation Level to Forecast Italian Industrial Production

J ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 508-560
Author(s):  
Riccardo Corradini
Keyword(s):  
State Space ◽  
Industrial Production ◽  
Forecast Error ◽  
Moving Average ◽  
Predictive Ability ◽  
State Space Models ◽  
Vector Autoregressive ◽  
Autoregressive Moving Average Model ◽  
New Strategy ◽  
Moving Average Model

Normally, econometric models that forecast the Italian Industrial Production Index do not exploit information already available at time t + 1 for their own main industry groupings. The new strategy proposed here uses state–space models and aggregates the estimates to obtain improved results. The performance of disaggregated models is compared at the same time with a popular benchmark model, a univariate model tailored on the whole index, with persistent not formally registered holidays, a vector autoregressive moving average model exploiting all information published on the web for main industry groupings. Tests for superior predictive ability confirm the supremacy of the aggregated forecasts over three steps horizon using absolute forecast error and quadratic forecast error as a loss function. The datasets are available online.

New Methodology for the Determination of the Vertical Center of Gravity of In-Service Semisubmersibles: Proposal and Numerical Assessment

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
Ivan N. Porciuncula ◽  
Claudio A. Rodríguez ◽  
Paulo T. T. Esperança
Keyword(s):  
Accurate Determination ◽  
Center Of Gravity ◽  
Practical Implementation ◽  
Numerical Assessment ◽  
Neutral Equilibrium ◽  
Inclination Angles ◽  
Numerical Tools ◽  
The Stability ◽  
Metacentric Height

Along its lifetime, an offshore unit is subjected to several equipment interventions. These modifications may include large conversions in loco that usually are not adequately documented. Hence, the accurate determination of the platform's center of gravity (KG) is not possible. For vessels with low metacentric height (GM), such as semisubmersibles, Classification Societies penalize the platform's KG, inhibiting the installation of new equipment until an accurate measurement of KG is provided, i.e., until an updated inclining test is performed. For an operating semisubmersible, the execution of this type of test is not an alternative because it implies in removing the vessel from its in-service location to sheltered waters. Relatively recently, some methods have been proposed for the estimation of KG for in-service vessels. However, as all of the methods depend on accurate measurements of inclination angles and, eventually, on numerical tools for the simulation of vessel dynamics onboard, they are not straightforward for practical implementation. The objective of the paper is to present a practical methodology for the experimental determination of KG, without the need of accurate measurements of inclinations and/or complex numerical simulations, but based on actual operations that can be performed onboard. Indeed, the proposed methodology relies on the search, identification, and execution of a neutral equilibrium condition where, for instance, KG = KM. The method is exemplified using actual data of a typical semisubmersible. The paper also numerically explores and discusses the stability of the platform under various conditions with unstable initial GM, as well as the effect of mooring and risers.

scholarly journals Co-integration with score-driven models: an application to US real GDP growth, US inflation rate, and effective federal funds rate

2021 ◽  
pp. 1-21
Author(s):  
Szabolcs Blazsek ◽  
Alvaro Escribano ◽  
Adrian Licht
Keyword(s):  
Inflation Rate ◽  
Predictive Accuracy ◽  
Moving Average ◽  
Impulse Response Functions ◽  
Federal Funds ◽  
Federal Funds Rate ◽  
Vector Autoregressive ◽  
Autoregressive Moving Average Model ◽  
Long Run ◽  
The Us

Abstract Nonlinear co-integration is studied for score-driven models, using a new multivariate dynamic conditional score/generalized autoregressive score model. The model is named t-QVARMA (quasi-vector autoregressive moving average model), which is a location model for the multivariate t-distribution. In t-QVARMA, I(0) and co-integrated I(1) components of the dependent variables are included. For t-QVARMA, the conditions of the maximum likelihood estimator and impulse response functions (IRFs) are presented. A limiting special case of t-QVARMA, named Gaussian-QVARMA, is a Gaussian-VARMA specification with I(0) and I(1) components. As an empirical application, the US real gross domestic product growth, US inflation rate, and effective federal funds rate are studied for the period of 1954 Q3 to 2020 Q2. Statistical performance and predictive accuracy of t-QVARMA are superior to those of Gaussian-VAR. Estimates of the short-run IRF, long-run IRF, and total IRF impacts for the US data are reported.

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