Hostname: page-component-6898cfcb55-cbk24 Total loading time: 0 Render date: 2026-11-11T07:48:34.647Z Has data issue: false hasContentIssue false
  • English
  • Français

Slip parameter estimation of a single wheel using a non-linear observer

Published online by Cambridge University Press:  09 October 2008

Z. B. Song,
L. D. Seneviratne,
K. Althoefer,
X. J. Song and
Y. H. Zweiri
Z. B. Song
Affiliation:
Division of Engineering, King's College London, Strand, London WC2R 2LS, UK
L. D. Seneviratne
Affiliation:
Division of Engineering, King's College London, Strand, London WC2R 2LS, UK
K. Althoefer
Affiliation:
Division of Engineering, King's College London, Strand, London WC2R 2LS, UK
X. J. Song*
Affiliation:
Division of Engineering, King's College London, Strand, London WC2R 2LS, UK
Y. H. Zweiri
Affiliation:
School of Engineering, Mútah University, Karak, Jordon, 61710
*
*Corresponding author. E-mail: song.xiaojing@kcl.ac.uk
Rights & Permissions [Opens in a new window]

Summary

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

Sliding mode observer is a variable structure system where the dynamics of a nonlinear system is altered via application of a high-frequency switching control. This paper presents a non-linear sliding mode observer for wheel linear slip and slip angle estimation of a single wheel based on its kinematic model and velocity measurements with added noise to simulate actual on-board sensor measurements. Lyapunov stability theory is used to establish the stability conditions for the observer. It is shown that the observer will converge in a finite time, provided the observer gains satisfy constraints based on a stability analysis. To validate the observer, linear and two-dimensional (2D) test rigs are specially designed. The sliding mode observer is tested under a variety of conditions and it is shown that the sliding mode observer can estimate wheel slip and slip angle to a high accuracy. It is also shown that the sliding mode observer can accurately predict wheel slip and slip angle in the presence of noise, by testing the performance of the sliding mode observer after adding white noise to the measurements. An extended Kalman filter is also developed for comparison purposes. The sliding mode observer is better in terms of prediction accuracy.

Keywords

Sliding mode observerWheel slip and slip angle estimationAutonomySingle wheelExtended Kalman filter

Information

Type
Article
Information
Robotica , Volume 27 , Issue 6 , October 2009, pp. 801 - 811
Copyright
Copyright © Cambridge University Press 2008

References

1.Bevly, D. M., Gerdes, J. C., Wilson, C. and Zhang, G., “The Use of GPS Based Velocity Measurements for Improved Vehicle State Estimation,” Proceeding of the American Control Conference, Chicago, IL (2000) pp. 2538–2542.Google Scholar
2.Lee, C., Hedrick, K. and Yi, K., “Real-time slip-based estimation of maximum tire-road friction coefficient,” IEEE/ASME Trans. Mechatron. 9 (2), 454–458 (Jun. 2004).CrossRefGoogle Scholar
3.Song, X. J., Seneviratne, L., Althoefer, K. and Song, Z. B., “Vision-based velocity estimation for unmanned ground vehicles,” Int. J. Info. Acquis. 4 (4), 303–325 (2007).CrossRefGoogle Scholar
4.Iagnemma, K. and Dubowsky, S., Mobile Robots in Rough Terrain: Estimation, Motion Planning and Control with Application to Planetary Rovers, vol. 12 (Springer-Verlag, Berlin, Heidenlberg, 2004).CrossRefGoogle Scholar
5.Ojeda, L., Cruz, D., Reina, G. and Borenstein, J., “Current-based slippage detection and odometry correction for mobile robots and planetary rovers,” IEEE Trans. Rob. 22, 366–378 (2006).CrossRefGoogle Scholar
6.Angelova, A., Matthies, L., Helmick, D., Sibley, G. and Perona, P., “Learning to Predict Slip for Ground Robots,” Proceedings of the IEEE International Conference on Robotics and Automation, Orlando, FL (2006) pp. 3324–3331.Google Scholar
7.Ray, L., “Nonlinear tire force estimation and road friction identification: Simulation and experiments,” Automatica 33 (10), 1819–1833 (1997).CrossRefGoogle Scholar
8.Le, A. T., “Estimation of Track-soil Interactions for Autonomous Tracked Vehicles,” Proceedings of the IEEE International Conference on Robotics and Automation, Albuquerque, New Mexico, New York, vol. 2 (1997) pp. 1388–1393.Google Scholar
9.Scheding, S., Dissanayake, G., Nebot, E. M. and Durrant-Whyte, H., “An experiment in autonomous navigation of an underground mining vehicle,” IEEE Trans. Rob. Automat. 15 (1), 85–95 (1999).CrossRefGoogle Scholar
10.Cherouat, H., Braci, M. and Diop, S., “Vehicle Velocity, Side Slip Angles and Yaw Rate Estimation,” Proceedings of the IEEE International Symposium on Industrial Electronics, Dubrovnik, Croatia, vol. 1 (2005) pp. 349–354.Google Scholar
11.Aoki, Y., Uchida, T. and Hori, Y., “Experimental Demonstration of Body Slip Angle Control Based on a Novel Linear Observer for Electric Vehicle,” Industrial Electronics Society, IECON, 31st Annual Conference of IEEE, Raleigh, North Carolina, USA (2005) pp. 2620–2625.Google Scholar
12.Ünsal, C. and Kachroo, P., “Sliding mode measurement feedback control for antilock braking systems,” IEEE Trans. Control Syst. Technol. 7 (2), 271–281 (1999).CrossRefGoogle Scholar
13.Stéphant, J. and Charara, A., “Virtual sensor: Application to vehicle sideslip angle and transversal forces,” IEEE Trans. Ind. Electron. 51 (2), 278–289 (Apr. 2004).CrossRefGoogle Scholar
14.Cadiou, J. C., Hadri, A. E. and Chikhi, F., “Non-Linear Tyre Forces Estimation Based on Vehicle Dynamics Observation in a Finite Time,” Proc. Inst. Mech. Eng. [D]: J. Automob. Eng. 218, 1379–1392 (Jul. 2004).CrossRefGoogle Scholar
15.Song, Z. B., Zweiri, Y. H., Seneviratne, L. D. and Althoefer, K., “Non-linear observer for slip estimation of tracked vehicles,” Proc. Inst. Mech. Eng. [D]: J. Automob. Eng. 222 (4), 515–533 (2008).CrossRefGoogle Scholar
16.Song, Z. B., Song, X. J., Althoefer, K., Zweiri, Y. and Seneviratne, L. D., “Non-Linear Observer for Slip Parameter Estimation of Unmanned Wheeled Vehicles,” IEEE International Conference on Mechatronics and Automation, Harbin, Heilongjiang, China (2007).CrossRefGoogle Scholar
17.Muir, P. F. and Pneuma, C. P., “Kinematic modelling of wheeled vehicle,” Int. J. Rob. Res. 4 (2), 281–329 (1987).Google Scholar
18.Alexander, J. C. and Haddocks, J. H., “On the kinematics of wheeled mobile robots,” Int. J. Rob. Res. 8 (5), 15–27 (1989).CrossRefGoogle Scholar
19.Shim, H. S., Kim, J. H. and Koch, K., “Variable Structure Control Nonholonomic Wheeled Mobile Robot,” Proceedings of the IEEE International Conference on Robotics and Automation, Nagoya, Japan (1995) pp. 1694–1700.Google Scholar
20.Murray, R. M. and Sastry, S. S., “Nonholonomic motion planning: Steering using sinusoids,” IEEE Trans. Automat. Control 38 (5), 700–716 (1993).CrossRefGoogle Scholar
21.Yoshida, K. and Ishigami, G., “Steering Characteristics of a Rigid Wheel for Exploration on Loose Soil,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan (2006) pp. 3995–4000.Google Scholar
22.Utkin, V., Guldner, J. and Shi, J., Sliding Mode Control in Electromechanical Systems (Taylor & Francis, Philadelphia, PA, 1999).Google Scholar
23.Edwards, C. and Spurgeon, S. K., Sliding Mode Control : Theory and Applications (Taylor & Francis, London, 1998).CrossRefGoogle Scholar
24.Zweiri, Y. H., Whidborne, J. F. and Seneviratne, L. D., “Diesel Engine Indicated and Load Torque Estimation Using Sliding Mode Observer,” Proc. Inst. Mech. Eng. [D]: J. Automob. Eng. 220 (6), 775–785 (2006).CrossRefGoogle Scholar
25.Le, A. T., Modelling and Control of Tracked Vehicles, Ph.D. Thesis (Department of Mechanical Engineering, The University of Sydney, Jan. 1999).Google Scholar
26.Racelogic Non-contact GPS speed and distance measurement, available at http://www.racelogic.co.uk/?show=VBOX (Last accessed on 15 Dec. 2007).Google Scholar
27.Helmick, D. M., Cheng, Y., Clouse, D. S. and Matthies, L. H., “Path Following using Visual Odometry for a Mars Rover in High-Slip Environments,” IEEE Aerospace Conference Proceedings, Big Sky, Montana (2004) pp. 772–789.Google Scholar
28.Song, X. J., Seneviratne, L. D., Althoefer, K., Song, Z. B. and Zweiri, Y., “Visual Odometry for Velocity Estimation of UGVs,” IEEE International Conference on Mechatronics and Automation, Harbin, Heilongjiang, China (2007).CrossRefGoogle Scholar
29.Grewal, M. S. and Andrews, A. P., Kalman Filtering Theory and Practice Using Matlab (John Wiley & Sons, Hoboken, New Jersey, 2001).Google Scholar