The Experts below are selected from a list of 7017 Experts worldwide ranked by ideXlab platform
Seibum B. Choi - One of the best experts on this subject based on the ideXlab platform.
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early detection of tire Road Friction coefficient based on pneumatic trail stiffness
Advances in Computing and Communications, 2016Co-Authors: Kyoungseok Han, Eunjae Lee, Seibum B. ChoiAbstract:This paper presents a method for estimating the maximum lateral tire-Road Friction coefficient and wheel side slip angle based on the pneumatic trail information that exhibits unique characteristics according to the Road surface conditions. The high sensitivity of the pneumatic trail for the wheel side slip angle enables the proposed observer to detect the peak tire-Road Friction coefficient in low slip regions. The conventional method that is highly dependent on the tire model has drawbacks due to model uncertainty. In order to overcome these shortcomings, the proposed method minimizes the use of existing tire models. In addition, traction force is also considered in this paper using a correction factor. The estimation results are obtained recursively under the persistent excitation condition. A simulation is conducted first in order to verify the performance of the proposed method using a combination of the Carsim and Matlab & Simulink. Then, vehicle experiments are conducted on a proving ground in order to verify the feasibility of the proposed method. The verification results reveal that the early detection of the maximum tire-Road Friction coefficient is possible with less excitation signals than the conventional methods.
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Robust estimation of maximum tire-Road Friction coefficient considering Road surface irregularity
International Journal of Automotive Technology, 2016Co-Authors: Y. Hwang, Seibum B. ChoiAbstract:An accurate estimation of the maximum tire-Road Friction coefficient may provide higher performance in a vehicle active safety control system. Unfortunately, real-time tire-Road Friction coefficient estimation is costly and necessitates additional sensors that must be installed and maintained at all times. This paper proposes an advanced longitudinal tire-Road Friction coefficient estimation method that is capable of considering irregular Road surfaces. The proposed algorithm uses a stiffness based estimation method, however, unlike previous studies, improvements were made by suggesting a third order model to solve problems related to nonlinear mu-slip curve. To attain the tire-Road Friction coefficient, real-time normalized force is obtained from the force estimator as exerted from the tire in the low slip region using the recursive least squares method. The decisive aspect of using the suggested algorithm lies in its low cost and versatility. It can be used under irregular Road conditions due to its capability of easily obtaining wheel speed and acceleration values from production cars. The newly improved algorithm has been verified to computer simulations as well as compact size cars on dry asphalt conditions.
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ACC - Early detection of tire-Road Friction coefficient based on pneumatic trail stiffness
2016 American Control Conference (ACC), 2016Co-Authors: Kyoungseok Han, Eunjae Lee, Seibum B. ChoiAbstract:This paper presents a method for estimating the maximum lateral tire-Road Friction coefficient and wheel side slip angle based on the pneumatic trail information that exhibits unique characteristics according to the Road surface conditions. The high sensitivity of the pneumatic trail for the wheel side slip angle enables the proposed observer to detect the peak tire-Road Friction coefficient in low slip regions. The conventional method that is highly dependent on the tire model has drawbacks due to model uncertainty. In order to overcome these shortcomings, the proposed method minimizes the use of existing tire models. In addition, traction force is also considered in this paper using a correction factor. The estimation results are obtained recursively under the persistent excitation condition. A simulation is conducted first in order to verify the performance of the proposed method using a combination of the Carsim and Matlab & Simulink. Then, vehicle experiments are conducted on a proving ground in order to verify the feasibility of the proposed method. The verification results reveal that the early detection of the maximum tire-Road Friction coefficient is possible with less excitation signals than the conventional methods.
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linearized recursive least squares methods for real time identification of tire Road Friction coefficient
IEEE Transactions on Vehicular Technology, 2013Co-Authors: Mooryong Choi, Jiwon Oh, Seibum B. ChoiAbstract:The tire-Road Friction coefficient is critical information for conventional vehicle safety control systems. Most previous studies on tire-Road Friction estimation have only considered either longitudinal or lateral vehicle dynamics, which tends to cause significant underestimation of the actual tire-Road Friction coefficient. In this paper, the parameters, including the tire-Road Friction coefficient, of the combined longitudinal and lateral brushed tire model are identified by linearized recursive least squares (LRLS) methods, which efficiently utilize measurements related to both vehicle lateral and longitudinal dynamics in real time. The simulation study indicates that by using the estimated vehicle states and the tire forces of the four wheels, the suggested algorithm not only quickly identifies the tire-Road Friction coefficient with great accuracy and robustness before tires reach their Frictional limits but successfully estimates the two different tire-Road Friction coefficients of the two sides of a vehicle on a split- μ surface as well. The developed algorithm was verified through vehicle dynamics software Carsim and MATLAB/Simulink.
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Linearized Recursive Least Squares Methods for Real-Time Identification of Tire–Road Friction Coefficient
IEEE Transactions on Vehicular Technology, 2013Co-Authors: Mooryong Choi, Jiwon J. Oh, Seibum B. ChoiAbstract:The tire-Road Friction coefficient is critical information for conventional vehicle safety control systems. Most previous studies on tire-Road Friction estimation have only considered either longitudinal or lateral vehicle dynamics, which tends to cause significant underestimation of the actual tire-Road Friction coefficient. In this paper, the parameters, including the tire-Road Friction coefficient, of the combined longitudinal and lateral brushed tire model are identified by linearized recursive least squares (LRLS) methods, which efficiently utilize measurements related to both vehicle lateral and longitudinal dynamics in real time. The simulation study indicates that by using the estimated vehicle states and the tire forces of the four wheels, the suggested algorithm not only quickly identifies the tire-Road Friction coefficient with great accuracy and robustness before tires reach their Frictional limits but successfully estimates the two different tire-Road Friction coefficients of the two sides of a vehicle on a split- μ surface as well. The developed algorithm was verified through vehicle dynamics software Carsim and MATLAB/Simulink.
Rajesh Rajamani - One of the best experts on this subject based on the ideXlab platform.
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Tire-Road Friction Measurement on Highway Vehicles
Mechanical Engineering Series, 2011Co-Authors: Rajesh RajamaniAbstract:This chapter focuses on real-time tire-Road Friction coefficient measurement systems that are aimed at estimating Friction coefficient and detecting abrupt changes in its value. The main type of Friction estimation systems presented here are systems that utilize longitudinal vehicle dynamics and longitudinal motion measurements. The algorithms and experimental results presented in this chapter are largely adapted from the paper published by Wang, et al. (2004).
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Estimation of Tire-Road Friction Coefficient Using a Novel Wireless Piezoelectric Tire Sensor
IEEE Sensors Journal, 2011Co-Authors: Gurkan Erdogan, Lee Alexander, Rajesh RajamaniAbstract:A tire-Road Friction coefficient estimation approach is proposed which makes use of the uncoupled lateral deflection profile of the tire carcass measured from inside the tire through the entire contact patch. The unique design of the developed wireless piezoelectric sensor enables the decoupling of the lateral carcass deformations from the radial and tangential deformations. The estimation of the tire-Road Friction coefficient depends on the estimation of slip angle, lateral tire force, aligning moment, and the use of a brush model. The tire slip angle is estimated as the slope of the lateral deflection curve at the leading edge of the contact patch. The portion of the deflection profile measured in the contact patch is assumed to be a superposition of three types of lateral carcass deformations, namely, shift, yaw, and bend. The force and moment acting on the tire are obtained by using the coefficients of a parabolic function which approximates the deflection profile inside the contact patch and whose terms represent each type of deformation. The estimated force, moment, and slip angle variables are then plugged into the brush model to estimate the tire-Road Friction coefficient. A specially constructed tire test rig is used to experimentally evaluate the performance of the developed estimation approach and the tire sensor. Experimental results show that the developed sensor can provide good estimation of both slip angle and tire-Road Friction coefficient.
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Tire-Road Friction-Coefficient Estimation
IEEE Control Systems, 2010Co-Authors: Rajesh Rajamani, Damrongrit Piyabongkarn, Jae Y. Lew, Gridsada PhanomchoengAbstract:Tire-Road forces are crucial in vehicle dynamics and control because they are the only forces that a vehicle experiences from the ground. These forces significantly affect the lateral, longitudinal, yaw, and roll behavior of the vehicle. The maximum force that a tire can supply is determined by the maximum value of the tire-Road Friction coefficient for a given normal vertical load on the tire. For each tire, the normalized traction force p, alternatively called the coefficient of traction, is defined as VfI + F (1) where Fχ, Fψ and Fζ are the longitudinal, lateral, and normal, that is, vertical, forces acting on the tire. The objective of Friction-coefficient estimation is to predict the maximum value of the normalized traction force p that each tire can provide. This value, which is called the tire-Road Friction coefficient μ, depends on the characteristics of the Road surface. The value of μ varies between zero and one depending on the type of Road surface under consideration, such as icy, snow covered, gravel, and dry asphalt.
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measurement of uncoupled lateral carcass deflections with a wireless piezoelectric sensor and estimation of tire Road Friction coefficient
ASME 2010 Dynamic Systems and Control Conference DSCC2010, 2010Co-Authors: Gurkan Erdogan, Lee Alexander, Rajesh RajamaniAbstract:A new tire-Road Friction coefficient estimation approach based on lateral carcass deflection measurements is proposed. The unique design of the developed wireless piezoelectric sensor decouples lateral carcass deformations from radial and tangential carcass deformations. The estimation of the tire-Road Friction coefficient depends on the estimation of the slip angle and the lateral tire force. The tire slip angle is estimated as the slope of the lateral deflection curve at the leading edge of the contact patch. The lateral tire force is obtained by using a parabolic relationship with the lateral deflections in the contact patch. The estimated slip angle and lateral force are then plugged into a tire brush model to estimate the tire-Road Friction coefficient. A specially constructed tire test-rig is used to experimentally evaluate the performance of the tire sensor and the developed approach. Experimental results show that the proposed tire-Road Friction coefficient estimation approach is quite promising.Copyright © 2010 by ASME
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gps based real time identification of tire Road Friction coefficient
IEEE Transactions on Control Systems and Technology, 2002Co-Authors: Jin-oh Hahn, Rajesh Rajamani, Lee AlexanderAbstract:Vehicle control systems such as collision avoidance, adaptive cruise control, and automated lane-keeping systems as well as ABS and stability control systems can benefit significantly from being made "Road-adaptive." The estimation of tire-Road Friction coefficient at the wheels allows the control algorithm in such systems to adapt to external driving conditions. This paper develops a new tire-Road Friction coefficient estimation algorithm based on measurements related to the lateral dynamics of the vehicle. A lateral tire force model parameterized as a function of slip angle, Friction coefficient, normal force and cornering stiffness is used. A real-time parameter identification algorithm that utilizes measurements from a differential global positioning system (DGPS) system and a gyroscope is used to identify the tire-Road Friction coefficient and cornering stiffness parameters of the tire. The advantage of the developed algorithm is that it does not require large longitudinal slip in order to provide reliable Friction estimates. Simulation studies indicate that a parameter convergence rate of 1 s can be obtained. Experiments conducted on both dry and slippery Road indicate that the algorithm can work very effectively in identifying a slippery Road.
Jadranko Matuško - One of the best experts on this subject based on the ideXlab platform.
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neural network based tire Road Friction force estimation
Engineering Applications of Artificial Intelligence, 2008Co-Authors: Jadranko Matuško, Ivan Petrovic, Nedjeljko PericAbstract:This paper deals with the problem of robust tire/Road Friction force estimation. Availability of actual value of the Friction force generated in contact between the tire and the Road has significant importance for active safety systems in modern cars, e.g. anti-lock brake systems, traction control systems, vehicle dynamic systems, etc. Since state estimators are usually based on the process model, they are sensitive to model inaccuracy. In this paper we propose a new neural network based estimation scheme, which makes Friction force estimation insensitive to modelling inaccuracies. The neural network is added to the estimator in order to compensate effects of the Friction model uncertainties to the estimation quality. An adaptation law for the neural network parameters is derived using Lyapunov stability analysis. The proposed state estimator provides accurate estimation of the tire/Road Friction force when Friction characteristic is only approximately known or even completely unknown. Quality of the estimation is examined through simulation using one wheel Friction model. Simulation results suggest very fast Friction force estimation and compensation of the changes of the model parameters even when they vary in wide range.
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Neural network based tire/Road Friction force estimation
Engineering Applications of Artificial Intelligence, 2008Co-Authors: Jadranko Matuško, Ivan Petrović, Nedjeljko PerićAbstract:This paper deals with the problem of robust tire/Road Friction force estimation. Availability of actual value of the Friction force generated in contact between the tire and the Road has significant importance for active safety systems in modern cars, e.g. anti-lock brake systems, traction control systems, vehicle dynamic systems, etc. Since state estimators are usually based on the process model, they are sensitive to model inaccuracy. In this paper we propose a new neural network based estimation scheme, which makes Friction force estimation insensitive to modelling inaccuracies. The neural network is added to the estimator in order to compensate effects of the Friction model uncertainties to the estimation quality. An adaptation law for the neural network parameters is derived using Lyapunov stability analysis. The proposed state estimator provides accurate estimation of the tire/Road Friction force when Friction characteristic is only approximately known or even completely unknown. Quality of the estimation is examined through simulation using one wheel Friction model. Simulation results suggest very fast Friction force estimation and compensation of the changes of the model parameters even when they vary in wide range.
Nedjeljko Peric - One of the best experts on this subject based on the ideXlab platform.
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neural network based tire Road Friction force estimation
Engineering Applications of Artificial Intelligence, 2008Co-Authors: Jadranko Matuško, Ivan Petrovic, Nedjeljko PericAbstract:This paper deals with the problem of robust tire/Road Friction force estimation. Availability of actual value of the Friction force generated in contact between the tire and the Road has significant importance for active safety systems in modern cars, e.g. anti-lock brake systems, traction control systems, vehicle dynamic systems, etc. Since state estimators are usually based on the process model, they are sensitive to model inaccuracy. In this paper we propose a new neural network based estimation scheme, which makes Friction force estimation insensitive to modelling inaccuracies. The neural network is added to the estimator in order to compensate effects of the Friction model uncertainties to the estimation quality. An adaptation law for the neural network parameters is derived using Lyapunov stability analysis. The proposed state estimator provides accurate estimation of the tire/Road Friction force when Friction characteristic is only approximately known or even completely unknown. Quality of the estimation is examined through simulation using one wheel Friction model. Simulation results suggest very fast Friction force estimation and compensation of the changes of the model parameters even when they vary in wide range.
Nedjeljko Perić - One of the best experts on this subject based on the ideXlab platform.
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Neural network based tire/Road Friction force estimation
Engineering Applications of Artificial Intelligence, 2008Co-Authors: Jadranko Matuško, Ivan Petrović, Nedjeljko PerićAbstract:This paper deals with the problem of robust tire/Road Friction force estimation. Availability of actual value of the Friction force generated in contact between the tire and the Road has significant importance for active safety systems in modern cars, e.g. anti-lock brake systems, traction control systems, vehicle dynamic systems, etc. Since state estimators are usually based on the process model, they are sensitive to model inaccuracy. In this paper we propose a new neural network based estimation scheme, which makes Friction force estimation insensitive to modelling inaccuracies. The neural network is added to the estimator in order to compensate effects of the Friction model uncertainties to the estimation quality. An adaptation law for the neural network parameters is derived using Lyapunov stability analysis. The proposed state estimator provides accurate estimation of the tire/Road Friction force when Friction characteristic is only approximately known or even completely unknown. Quality of the estimation is examined through simulation using one wheel Friction model. Simulation results suggest very fast Friction force estimation and compensation of the changes of the model parameters even when they vary in wide range.
