Numerical generation of road profile through spectral description for simulation of vehicle suspension

Dharankar, Chandrashekhar S.; Hada, Mahesh Kumar; Chandel, Sunil

doi:10.1007/s40430-016-0615-6

Cited by 25 publications

(13 citation statements)

References 14 publications

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“…(b). The differential equation in time domain can be formulated as

{\dot{z}}_{r} ((), t) = 2 π f_{0} V z_{r} ((), t) + n_{0} \sqrt{2 π G_{q} ((), n_{0}) V} W_{n} ((), t)

where W n ( t ) is white noise with 0 mean value and 0.1 intensity power spectrum density, f 0 is the low cut‐off frequency with value of 0.11 m ‐1 , n 0 is the reference spatial frequency with value of 0.1 m ‐1 , and G q ( n 0 ) = 256 × 10 −6 m 3 denotes the road roughness coefficient. For the convenience of comparison, the forward velocity V = 15 m/s is chosen . To compare the simulation results with the other pertinent references, superposition of multi‐nonlinear functions with different frequency are chosen, as shown in Fig.…”

Section: Simulation Resultsmentioning

confidence: 99%

Novel Optimal Design Approach for Output‐Feedback H_∞ Control of Vehicle Active Seat‐Suspension System

Wei

Cai

Zhang

et al. 2018

Asian Journal of Control

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In this paper, the output-feedback control problem of a vehicle active seat-suspension system is investigated. A novel optimal design approach for an output-feedback H ∞ controller is proposed. The main objective of the controller is to minimize the seat vertical acceleration to improve vehicle ride comfort. First, the human body and the seat are considered in the modeling of a vehicle active suspension system, which makes the model more precise. Other constraints, such as tire deflection, suspension deflection and actuator saturation, are also considered. Then the output-feedback control strategy is adopted since some state variables, such as body acceleration and body deflection, are unavailable. A concise and effective approach for an output-feedback H ∞ optimal control is presented. The desired controller is obtained by solving the corresponding linear matrix inequalities (LMIs) and by the calculation of equations proposed in this paper. Finally, a numerical example is presented to show the effectiveness and advantages of the proposed controller design approach.

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“…(b). The differential equation in time domain can be formulated as

{\dot{z}}_{r} ((), t) = 2 π f_{0} V z_{r} ((), t) + n_{0} \sqrt{2 π G_{q} ((), n_{0}) V} W_{n} ((), t)

Section: Simulation Resultsmentioning

confidence: 99%

Novel Optimal Design Approach for Output‐Feedback H_∞ Control of Vehicle Active Seat‐Suspension System

Wei

Cai

Zhang

et al. 2018

Asian Journal of Control

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“…Within the spatial frequency scope of n 1 < n < n 2 , the power spectrum density of bridge deck roughness is G q n . Based on a frequency spectrum 3 Complexity spreading form of the steady random process, the variance σ 2 q of the bridge deck roughness could be expressed by [17][18][19][20][21]…”

Section: Vibration Responses Of Long-span Bridgesmentioning

confidence: 99%

The Performance Study on the Long‐Span Bridge Involving the Wireless Sensor Network Technology in a Big Data Environment

Zhang

Sun

et al. 2018

Complexity

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The random traffic flow model which considers parameters of all the vehicles passing through the bridge, including arrival time, vehicle speed, vehicle type, vehicle weight, and horizontal position as well as the bridge deck roughness, is input into the vehiclebridge coupling vibration program. In this way, vehicle-bridge coupling vibration responses with considering the random traffic flow can be numerically simulated. Experimental test is used to validate the numerical simulation, and they had the consistent changing trends. This result proves the reliability of the vehicle-bridge coupling model in this paper. However, the computational process of this method is complicated and proposes high requirements for computer performance and resources. Therefore, this paper considers using a more advanced intelligent method to predict vibration responses of the long-span bridge. The PSO-BP (particle swarm optimization-back propagation) neural network model is proposed to predict vibration responses of the long-span bridge. Predicted values and real values at each point basically have the consistent changing trends, and the maximum error is less than 10%. Hence, it is feasible to predict vibration responses of the long-span bridge using the PSO-BP neural network model. In order to verify advantages of the predicting model, it is compared with the BP neural network model and GA-BP neural network model. The PSO-BP neural network model converges to the set critical error after it is iterated to the 226th generation, while the other two neural network models are not converged. In addition, the relative error of predicted values using PSO-BP neural network is only 2.71%, which is obviously less than the predicted results of other two neural network models. We can find that the PSO-BP neural network model proposed by the paper in predicting vibration responses is highly efficient and accurate.

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“…Where σ 2 is the pavement profile variance, v is the vehicle velocity, while α is a constant that depends on the type of pavement surface, it is recommended to use following α values (Dharankar, Hada, & Chandel, 2016;Tyan et al, 2009). …”

Section: Shaping Filter Approachmentioning

confidence: 99%

Evaluation of the methodologies used to generate random pavement profiles based on the power spectral density: An approach based on the International Roughness Index

Goenaga¹,

Fuentes²,

Mora³

2017

ing.inv.

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The pavement roughness is the main variable that produces the vertical excitation in vehicles. Pavement profiles are the main determinant of (i) discomfort perception on users and (ii) dynamic loads generated at the tire-pavement interface; hence its evaluation constitutes an essential step on a Pavement Management System. The present document evaluates two specific techniques used to simulate pavement profiles; these are the shaping filter and the sinusoidal approach, both based on the Power Spectral Density. Pavement roughness was evaluated using the International Roughness Index (IRI), which represents the most used index to characterize longitudinal road profiles. Appropriate parameters were defined in the simulation process to obtain pavement profiles with specific ranges of IRI values using both simulation techniques. The results suggest that using a sinusoidal approach one can generate random profiles with IRI values that are representative of different road types; therefore, one could generate a profile for a paved or an unpaved road, representing all the proposed categories defined by ISO 8608 standard. On the other hand, to obtain similar results using the shaping filter approximation, a modification in the simulation parameters is necessary. The new proposed values allow one to generate pavement profiles with high levels of roughness, covering a wider range of surface types. Finally, the results of the current investigation could be used to further improve our understanding on the effect of pavement roughness on tire pavement interaction. The evaluated methodologies could be used to generate random profiles with specific levels of roughness to assess its effect on dynamic loads generated at the tire-pavement interface and user's perception of road condition.Keywords: Random process, power spectral pensity, sinusoidal approximation, shaping filter, pavement profile, roughness. RESUMENLa rugosidad del pavimento es la principal variable que produce la excitación vertical en los vehículos. Los perfiles de pavimento son los principales responsables de (i) la incomodidad percibida por los usuarios y, (ii) las cargas dinámicas generadas en la interfaz entre el neumático y el pavimento, de ahí que su evaluación constituye un paso esencial en un Sistema de Gestión de Pavimentos. El presente documento evalúa dos técnicas específicas utilizadas para simular perfiles de pavimento; estos son los filtros de forma y la aproximación sinusoidal, ambos basados en la Densidad de Potencia Espectral. La rugosidad del pavimento se evaluó utilizando el Índice Rugosidad Internacional (IRI), el cual es el índice más utilizado para caracterizar rugosidad del perfil longitudinal de una vía. En la presente investigación se definieron parámetros apropiados en el proceso de simulación para obtener perfiles de pavimento con rangos específicos de los valores de IRI utilizando ambas técnicas de simulación. Los resultados sugieren que el uso de la aproximación sinusoidal puede generar perfiles aleatorios con valores de IRI que...

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Numerical generation of road profile through spectral description for simulation of vehicle suspension

Cited by 25 publications

References 14 publications

Novel Optimal Design Approach for Output‐Feedback H_∞ Control of Vehicle Active Seat‐Suspension System

Novel Optimal Design Approach for Output‐Feedback H_∞ Control of Vehicle Active Seat‐Suspension System

The Performance Study on the Long‐Span Bridge Involving the Wireless Sensor Network Technology in a Big Data Environment

Evaluation of the methodologies used to generate random pavement profiles based on the power spectral density: An approach based on the International Roughness Index

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Numerical generation of road profile through spectral description for simulation of vehicle suspension

Cited by 25 publications

References 14 publications

Novel Optimal Design Approach for Output‐Feedback H∞ Control of Vehicle Active Seat‐Suspension System

Novel Optimal Design Approach for Output‐Feedback H∞ Control of Vehicle Active Seat‐Suspension System

The Performance Study on the Long‐Span Bridge Involving the Wireless Sensor Network Technology in a Big Data Environment

Evaluation of the methodologies used to generate random pavement profiles based on the power spectral density: An approach based on the International Roughness Index

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Product

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Novel Optimal Design Approach for Output‐Feedback H_∞ Control of Vehicle Active Seat‐Suspension System

Novel Optimal Design Approach for Output‐Feedback H_∞ Control of Vehicle Active Seat‐Suspension System