Research Papers

An Intelligent Tire Based Tire-Road Friction Estimation Technique and Adaptive Wheel Slip Controller for Antilock Brake System

[+] Author and Article Information
Kanwar B. Singh

e-mail: kbsingh@vt.edu

Mustafa Ali Arat

e-mail: marat@vt.edu

Saied Taheri

e-mail: staheri@vt.edu
Intelligent Transportation Laboratory (ITL),
Department of Mechanical Engineering,
Virginia Tech,
Blacksburg, VA 24061

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the Journal of Dynamic Systems, Measurement, and Control. Manuscript received August 14, 2011; final manuscript received July 18, 2012; published online February 21, 2013. Assoc. Editor: Luis Alvarez.

J. Dyn. Sys., Meas., Control 135(3), 031002 (Feb 21, 2013) (26 pages) Paper No: DS-11-1256; doi: 10.1115/1.4007704 History: Received August 14, 2011; Revised July 18, 2012

The contact between the tire and the road is the key enabler of vehicle acceleration, deceleration and steering. However, due to changes to the road conditions, the driver's ability to maintain a stable vehicle may be at risk. In many cases, this requires intervention from the chassis control systems onboard the vehicle. Although these systems perform well in a variety of situations, their performance can be improved if a real-time estimate of the tire-road friction coefficient is available. Existing tire-road friction estimation approaches often require certain levels of vehicle longitudinal and/or lateral motion to satisfy the persistence of excitation condition for reliable estimations. Such excitations may undesirably interfere with vehicle motion controls. This paper presents a novel development and implementation of a real-time tire-road contact parameter estimation methodology using acceleration signals from an intelligent tire. The proposed method characterizes the terrain using the measured frequency response of the tire vibrations and provides the capability to estimate the tire road friction coefficient under extremely lower levels of force utilization. Under higher levels of force excitation (high slip conditions), the increased vibration levels due to the stick/slip phenomenon linked to the tread block vibration modes make the proposed tire vibrations based method unsuitable. Therefore for high slip conditions, a brush model-based nonlinear least squares (NLLS) parameter estimation approach is proposed. Hence an integrated approach using the intelligent tire based friction estimator and the model based estimator gives us the capability to reliably estimate friction for a wider range of excitations. Considering the strong interdependence between the operating road surface condition and the instantaneous forces and moments generated; this real time estimate of the tire-road friction coefficient is expected to play a pivotal role in improving the performance of a number of vehicle control systems. In particular, this paper focuses on the possibility of enhancing the performance of the ABS control systems. In order to achieve the aforementioned objectives, the design and implementation of a fuzzy/sliding mode/proportional integral (fuzzy-SMC-PI (FSP)) control methodology is proposed. The results show significant improvements in the stopping distance of a vehicle equipped with an intelligent tire based FSP controller as compared to a vehicle equipped with a standard ABS.

Copyright © 2013 by ASME
Your Session has timed out. Please sign back in to continue.


Wong, J. Y., 2001, Theory of Ground Vehicles, John Wiley and Sons, Inc., New York.
Leiber, H., and Czinczel, A., 1983, “Four Years of Experience With 4-Wheel Antiskid Brake Systems (ABS),” SAE, Paper No. 830481. [CrossRef]
Leiber, H., and Czinczel, A., 1979, “Antiskid System for Passenger Cars With a Digital Electronic Control Unit,” SAE, Paper No. 790458. [CrossRef]
Robert Bosch GmbH, 2000, Automative Handbook, 5th ed., Troy, MI.
Day, T., and Roberts, S. G., 2002, “A Simulation Model for Vehicle Braking Systems Fitted With ABS,” SAE, Paper No. 2002-01-0559. [CrossRef]
Taborek, J. J., 1957, Mechanics of Vehicles, Penton, Cleveland, OH.
Pacejka, H. B., 2006, Tyre and Vehicle Dynamics, 2nd ed., Butterworth-Heinemann, Oxford, UK.
Tan, H., and Tomizuka, M., 1990, “An Adaptive Sliding Mode Vehicle Traction Controller Design,” Proceedings of the American Control Conference, Vol. 2, pp. 1856–1861.
Gillespie, T., 1992, Fundamentals of Vehicle Dynamics, Society of Automotive Engineers, Warrendale, PA.
Yi, K., and Chung, J., 2001, “Nonlinear Brake Control for Vehicle CW/CA Systems,” IEEE/ASME Trans. Mechatron., 6, pp. 17–25. [CrossRef]
Dytran, “Mini Triaxial Accelerometers Specification Sheet,” http://www.dytran.com
Bozick, T., 2009, “Institute Researchers Develop New Way to Test Tires,” Institute for Advanced Learning and Research, Danville, VA, http://www.ialr.org
Welch, P., 1967, “The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms,” IEEE Trans. Audio Electroacoust., 15, pp. 70–73. [CrossRef]
Klir, G., and Yuan, B., 1995, Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall, Upper Saddle River, NJ.
Klir, G. J., 1999, “On Fuzzy-Set Interpretation of Possibility Theory,” Fuzzy Sets Syst., 108(3), pp. 263–273. [CrossRef]
Pacejka, H. B., and Bakker, E., 1992, “The Magic Formula Tyre Model,” Veh. Syst. Dyn., 21, pp. 1–18. [CrossRef]
Baffet, G., Charara, A., and Lechner, D., 2009, “Estimation of Vehicle Sideslip, Tire Force and Wheel Cornering Stiffness,” Control Eng. Pract., 17(11), pp. 1255–1264. [CrossRef]
Savaresi, S. M., and Tanelli, M., 2010, Active Braking Control Systems Design for Vehicles, Springer-Verlag, London, UK.
Duda, R., Hart, P., and Stork, D., 2001, Pattern Classification, 2nd ed., John Wiley & Sons Inc., New York.
Hagan, M. T., and Menhaj, M. B., 1994, “Training Feedforward Networks With the Marquardt Algorithm,” IEEE Trans. Neural Netw., 5, pp. 989–993. [CrossRef] [PubMed]
Slotine, J., and Li, W., 1991, Applied Nonlinear Control, Prentice Hall, Upper Saddle River, NJ.
Stanislaw, Z. H., 2003, Systems and Control, Oxford University Press, New York.
Kueon, Y. S., and Bedi, J. S., 1995, “Fuzzy-Neural-Sliding Mode Controller and Its Applications to the Vehicle Anti-Lock Braking Systems,” Proceedings IEEE/IAS Confernce Industrial Automation and Control: Emerging Technology, pp. 391–398. [CrossRef]
Edwards, C., and Spurgeon, K., 1998, Sliding Mode Control: Theory and Applications, Taylor and Francis, London, UK.
Kachroo, P., and Tomizuka, M., 1996, “Chattering Reduction and Error Convergence in the Sliding-Mode Control of a Class of Nonlinear Systems,” IEEE Trans. Autom. Control, 41(7), pp. 1063–1068. [CrossRef]
Ebrahimirad, H., Yazdanpanah, M., and Kazemi, R. J., 2004, “Sliding Mode Four Wheel Slip-Ratio Control of Anti-Lock Braking Systems,” IEEE International Conference on Industrial Technology, Vol. 3, pp. 1602–1606. [CrossRef]


Grahic Jump Location
Fig. 1

Free body diagram of the quarter car model

Grahic Jump Location
Fig. 2

Intelligent tire application: (a) sensor mounting location, (b) instrumented tire assembly, (c) mobile tire test rig, and (d) test rig attached to the towing vehicle

Grahic Jump Location
Fig. 3

Measured acceleration signal for one rotation

Grahic Jump Location
Fig. 4

Measured circumferential acceleration signal under free-rolling, traction, braking, and steering conditions

Grahic Jump Location
Fig. 10

Input MF: (a) tire speed, (b) tire pressure, and (c) vibration ratio; output MF: (d) terrain type

Grahic Jump Location
Fig. 11

Flowchart of the proposed terrain classification algorithm

Grahic Jump Location
Fig. 12

Tire tested on different road surface conditions: (a) rough asphalt, (b) regular asphalt, (c) smooth asphalt, and (d) wet asphalt

Grahic Jump Location
Fig. 13

Performance of the fuzzy logic classifier—low-slip conditions

Grahic Jump Location
Fig. 14

Performance of the fuzzy logic classifier—high-slip conditions

Grahic Jump Location
Fig. 15

Circumferential acceleration signal under low-slip conditions (top), and increased vibration levels in the circumferential acceleration signal under high-slip conditions (bottom)

Grahic Jump Location
Fig. 5

Tire tested on different road surface conditions: (a) dry surface testing, and (b) wet surface testing; roughness dependence study: (c) radial signal PSD, and (d) circumferential signal PSD

Grahic Jump Location
Fig. 6

(a) Accelerometer signal domains; PSD waveforms using: (b) all the domains, (c) only the pre-trailing domain, and (c) only the post-trailing domain

Grahic Jump Location
Fig. 7

High and low frequency domains in the circumferential acceleration PSD

Grahic Jump Location
Fig. 8

Vibration ratio on dry and wet surface conditions for a range of tire speeds

Grahic Jump Location
Fig. 9

Fuzzy logic based controller architecture

Grahic Jump Location
Fig. 16

Adaptation of the brush model toward the magic formula data

Grahic Jump Location
Fig. 17

Tire force estimator architecture

Grahic Jump Location
Fig. 18

Straight-line braking test—SMC observer—tire force estimates

Grahic Jump Location
Fig. 19

Straight-line braking test—estimated vehicle speed compared with the reference vehicle speed (top), and estimated wheel slip compared with the reference wheel slip (bottom)

Grahic Jump Location
Fig. 20

Measured radial and circumferential acceleration signal for one tire rotation

Grahic Jump Location
Fig. 21

Architecture of the proposed ANN model

Grahic Jump Location
Fig. 23

Friction estimation results using the brush model based algorithm under high-slip conditions

Grahic Jump Location
Fig. 24

Architecture of the proposed integrated approach using an intelligent tire based friction estimator and the model based estimator

Grahic Jump Location
Fig. 31

Implementation of the proposed FSP ABS

Grahic Jump Location
Fig. 30

Comparison of performance of the two ABS algorithms (a) high-μ, (b) high-μ, (c) low-μ, and (d) low-μ

Grahic Jump Location
Fig. 29

SMC based ABS model performance (a) high-μ and (b) low-μ

Grahic Jump Location
Fig. 28

Baseline ABS model performance (a) high-μ and (b) low-μ

Grahic Jump Location
Fig. 26

Flowchart of the used baseline ABS algorithm

Grahic Jump Location
Fig. 25

Baseline ABS model

Grahic Jump Location
Fig. 35

Intelligent tire based ABS with road-condition estimator and brake preconditioning module

Grahic Jump Location
Fig. 36

Jump-μ test results (a) no intelligent tire and no brake preconditioning, (b) with intelligent tire and no brake preconditioning, and (c) with intelligent tire and brake preconditioning

Grahic Jump Location
Fig. 37

Jump-μ results for extreme surface friction conditions (high=1-low=0.2-high=1) (a) without brake preconditioning and (b) with brake preconditioning

Grahic Jump Location
Fig. 38

Jump-μ results for extreme surface friction conditions (low = 0.2-high = 1-low = 0.2) (a) and (b)

Grahic Jump Location
Fig. 32

Fuzzy logic membership functions

Grahic Jump Location
Fig. 33

FSP based ABS performance (a) high-μ and (b) low-μ




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In