Parametric identification of vehicle’s structural characteristics

Koulocheris, Dimitrios; Dertimanis, Vasilis K.; Spentzas, C.N.

doi:10.1007/s10010-008-0066-z

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…Huh et al designed a vehicle mass estimator for adaptive roll stability control [6]. Koulocheris et al applied a parametric identification method to estimate structural parameters of commercial passenger vehicle [7]. All of these methods can only identify the part of physical parameters of vehicle.…”

Section: Introductionmentioning

confidence: 99%

A New Physical Parameter Identification Method for Two-Axis On-Road Vehicles: Simulation and Experiment

Zheng

Peng

Zhang

et al. 2015

Shock and Vibration

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A new physical parameter identification method for two-axis on-road vehicle is presented. The modal parameters of vehicle are identified by using the State Variable Method. To make it possible to determine the matricesM,C, andKof the vehicle, a known mass matrixΔMis designed to add into the vehicle in order to increase the number of equations ensuring that the number of equations is more than the one of unknowns. Therefore, the physical parameters of vehicle can be estimated by using the least square method. To validate the presented method, a numerical simulation example and an experiment example are given in this paper. The numerical simulation example shows that the largest of absolute value of percentage error is 1.493%. In the experiment example, a school bus is employed in study for the parameter identification. The simulation result from full-car model with the estimated physical parameters is compared with the test result. The agreement between the simulation and the test proves the effectiveness of the proposed estimation method.

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Section: Introductionmentioning

confidence: 99%

A New Physical Parameter Identification Method for Two-Axis On-Road Vehicles: Simulation and Experiment

Zheng

Peng

Zhang

et al. 2015

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…The process of identifying discrete-time models from multichannel excitation and response (or response only) measurements clearly falls within the aforementioned framework. Indeed, time-series analysis (Lu¨tkepohl, 2005;Box et al, 2008) and system identification (Ljung, 1999) have shown to be effective and valuable tools that retain crucial technological importance in the fields of data-driven modeling (Papakos and Fassois, 2003;Andersen, 1997;Koulocheris et al, 2008;Bayraktar et al, 2011), control (Landau andZito, 2006;De Korte, 2009) and structural health monitoring (Farrar and Worden, 2006;Farrar and Lieven, 2007;Dertimanis and Koulocheris, 2008;Morassi and Vestroni, 2008). This is why the implementation of corresponding techniques to structural systems (airplanes, bridges, oil platforms, vehicles, buildings, etc.…”

Section: Introductionmentioning

confidence: 99%

On the use of dispersion analysis for model assessment in structural identification

Dertimanis

2013

Journal of Vibration and Control

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One of the most important issues faced in parametric time-domain identification and subsequent experimental/ operational modal analysis is the correct estimation of model order, which in turn determines the number of structural vibration modes. The aim of this study is to provide a quantitative and physically meaningful framework for model order assessment that is characterized by global applicability, in the sense of implementation in both state-space and transfer function model representations. To this end and under the assumption of stationary wideband excitation, a novel dispersion analysis scheme is proposed for the quantification of every mode's relative importance to the total stochastic response, which is based on a modal decomposition of the covariance matrix. Subsequently, after defining the modal dispersion matrix, a corresponding metric is introduced and used either as a stand alone tool for model order assessment, or as an extension of existing tools, such as stabilization diagrams. The method is validated through both simulated (NASA Mini-Mast truss) and experimental (suspended steel subframe flexible structure) identification problems, for which a subspace and a prediction-error estimation method are utilized and compared under the proposed quantitative indices. Moreover, performance comparisons with other energy-based metrics are also reported. The results indicate that the proposed method can be effectively used in parametric time-domain structural identification, for both order assessment and comparison of diverse model-based estimation methods.

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“…The analysis of vector time-series, generally referring to the determination of the dynamics that govern the performance of a system under unobservable excitations, has been a subject of constant development for more than two decades, as part of the broader system identification framework. Relative applications are extended from econometrics (Clements and Henry, 1998;Lütkepohl, 2005), to dynamics (Ljung, 1999;Koulocheris et al, 2008), vibration (Papakos and Fassois, 2003), modal analysis (Huang, 2001) and fault diagnosis (Dertimanis, 2006).…”

Section: Introductionmentioning

confidence: 99%

On the State–space Realization of Vector Autoregressive Structures: An Assessment

Dertimanis

Koulocheris

2009

Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics

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This study explores the interconnection between vector autoregressive (VAR) structures and state-space models and results in a compact framework for the representation of multivariate time-series, as well as the estimation of structural information. The corresponding methodology that is developed, applies the fact that every VAR process of order n may be described by an equivalent (non-unique) VAR model of first order, which is identical to a state-space realization. The latter uncovers many "hidden" information of the initial model, it is more easy to manipulate and maintains significant second moments' information that can be reflected back to the original structure with no effort. The performance of the proposed framework is validated using vector time-series signatures from a structural system with two degrees of freedom, which retains a pair of closely spaced vibration modes and has been reported in the relevant literature.

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Parametric identification of vehicle’s structural characteristics

Cited by 9 publications

References 19 publications

A New Physical Parameter Identification Method for Two-Axis On-Road Vehicles: Simulation and Experiment

A New Physical Parameter Identification Method for Two-Axis On-Road Vehicles: Simulation and Experiment

On the use of dispersion analysis for model assessment in structural identification

On the State–space Realization of Vector Autoregressive Structures: An Assessment

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