A Predictive Controller for Object Tracking of a Mobile Robot

Xiaowei Zhou, Plamen Angelov, Chengwei Wang



In this paper a predictive controller for real-time target tracking in mobile robotics is proposed based on adaptive/evolving Takagi-Sugeno fuzzy systems, eTS. The predictive controller consists of two modules; i) a conventional fuzzy controller for robot motion control, and ii) a modelling tool for estimation of the target movements. The prediction of target movements enables the controller to be aware and to respond to the target movement in advance. Successful prediction will minimise the response delay of the conventional controller and improve the control quality. The model learning using eTS is fully automatic and performed ‘on fly’, ‘from scratch’. Data are processed in ‘one-pass’ manner, therefore it requires very limited computational resource and is suitable for on-board implementation on the mobile robots. Predictions are made in real-time. The same technique also has the potential to be used in the process control. Two reference controllers, a controller based on the Mamdani-Type fuzzy rule-base, and a controller based on the simple linear model, are also implemented in order to verify the proposed predictive controller. Experiments are carried out with a real mobile robot Pioneer 3DX. The performance of the three controllers is analyzed and compared.


  1. Liu P. X., M. Q.-H. Meng (2004) Online Data-Driven Fuzzy Clustering with Applications to Real-Time Robotic Tracking, IEEE Transactions on Fuzzy Systems, vol.12, No 4, 2004, pp.516-523.
  2. Astrom K. and B. Wittenmark (1984) Computer Controlled Systems: Theory and Design, Prentice Hall: NJ USA, 1984.
  3. Carse B., T.C. Fogarty, A. Munro (1996) Evolving Fuzzy Rule-based Controllers using GA, Fuzzy Sets and Systems, v.80, pp.273-294.
  4. Clarke D, Advances in Model-based Predictive Control, Oxford University Press, Oxford, UK, 1994.
  5. Babuska R (1998) Fuzzy Modelling for Control, Kluwer Publishers, Dordrecht, The Netherlands.
  6. Angelov, P., Evolving Rule-based Models: A Tool for Design of Flexible Adaptive Systems. Berlin, Germany: Springer Verlag, 2002.
  7. Angelov, P., Filev, D., “An Approach to On-line Identification of Takagi-Sugeno Fuzzy Models”, IEEE Transactions on System, Man, and Cybernetics, part B - Cybernetics, vol.34, No1, 2004, pp.484-498. ISSN 1094-6977.
  8. Angelov P and X Zhou (2006) Evolving Fuzzy Systems from Data Streams in Real-Time, In Proc. 2006 International Symposium on Evolving Fuzzy Systems, Ambelside, Lake District, UK, IEEE Press, pp.29-35, ISBN 0-7803-9719-3.
  9. Zhou. X., P. Angelov, An Approach to Autonomous Self-localization of a Mobile Robot in Completely Unknown Environment using Evolving Fuzzy Rule-based Classifier, First 2007 IEEE International Conference on Computational Intelligence Applications for Defense and Security, April 1-5, 2007, Honolulu, Hawaii, USA, pp.131-138.
  10. Yager R R and D P Filev (1993) Learning of Fuzzy Rules by Mountain Clustering, Proc. of SPIE Conf. on Application of Fuzzy Logic Technology, Boston, MA, USA, pp.246-254.
  11. Kailath, T, Linear systems, Prentice Hall, US, 1980.
  12. Psaltis D, A Sideris, A A Yamamura (1998) A Multilayered Neural Network Controller, Control Systems Magazine, vol. 8, pp. 17-21.
  13. Angelov P P (2004) A Fuzzy Controller with Evolving Structure, Information Sciences, ISSN 0020-0255, vol.161, pp.21-35.
  14. Andersen H.C., F.C. Teng, A.C. Tsoi (1994) Single Net Indirect Learning Architecture, IEEE Transactions on Neural Networks, v.5 (6), pp.1003-1005.
  15. Pioneer-3DX (2004) User Guide, ActiveMedia Robotics, Amherst, NH, USA.
  16. Jang, J.-S., RANFIS: adaptive-network-based fuzzy inference system, IEEE Transactions on System, Man, and Cybernetics, vol.23, No3, 1993, pp.665-685.
  17. Angelov P., R. Ramezani, X. Zhou, Autonomous Novelty Detection and Object Tracking in Video Streams using Evolving Clustering and Takagi-Sugeno type Neuro-Fuzzy System, 2008 IEEE Intern. Conf. on Fuzzy Syst. within the IEEE World Congress on Computational Intelligence, Hong Kong, June 1-6, 2008, to appear.

Paper Citation

in Harvard Style

Zhou X., Angelov P. and Wang C. (2008). A Predictive Controller for Object Tracking of a Mobile Robot . In Proceedings of the 2nd International Workshop on Intelligent Vehicle Control Systems - Volume 1: IVCS, (ICINCO 2008) ISBN 978-989-8111-34-0, pages 73-82. DOI: 10.5220/0001509600730082

in Bibtex Style

author={Xiaowei Zhou and Plamen Angelov and Chengwei Wang},
title={A Predictive Controller for Object Tracking of a Mobile Robot},
booktitle={Proceedings of the 2nd International Workshop on Intelligent Vehicle Control Systems - Volume 1: IVCS, (ICINCO 2008)},

in EndNote Style

JO - Proceedings of the 2nd International Workshop on Intelligent Vehicle Control Systems - Volume 1: IVCS, (ICINCO 2008)
TI - A Predictive Controller for Object Tracking of a Mobile Robot
SN - 978-989-8111-34-0
AU - Zhou X.
AU - Angelov P.
AU - Wang C.
PY - 2008
SP - 73
EP - 82
DO - 10.5220/0001509600730082