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Robust Adaptive Fuzzy Backstepping Control for Trajectory Tracking of an Electrically Driven Nonholonomic Mobile Robot with Uncertainties

불확실성을 가지는 전기 구동 논홀로노믹 이동 로봇의 궤적 추종을 위한 강인 적응 퍼지 백스테핑 제어

  • 신진호 (동의대학교 공과대학 메카트로닉스공학과)
  • Received : 2012.07.06
  • Accepted : 2012.08.27
  • Published : 2012.10.01

Abstract

This paper proposes a robust adaptive fuzzy backstepping control scheme for trajectory tracking of an electrically driven nonholonomic mobile robot with uncertainties and actuator dynamics. A complete model of an electrically driven nonholonomic mobile robot described in this work includes all models of the uncertain robot kinematics with a nonholonomic constraint, the uncertain robot body dynamics with uncertain frictions and unmodeled disturbances, and the uncertain actuator dynamics with disturbances. The proposed control scheme uses the backstepping control approach through a kinematic controller and a robust adaptive fuzzy velocity tracking controller. The presented control scheme has a voltage control input with an auxiliary current control input rather than a torque control input. It has two FBFNs(Fuzzy Basis Function Networks) to approximate two unknown nonlinear robot dynamic functions and a robust adaptive control input with the proposed adaptive laws to overcome the uncertainties such as parameter uncertainties and external disturbances. The proposed control scheme does not a priori require the accurate knowledge of all parameters in the robot kinematics, robot dynamics and actuator dynamics. It can also alleviate the chattering of the control input. Using the Lyapunov stability theory, the stability of the closed-loop robot control system is guaranteed. Simulation results show the validity and robustness of the proposed control scheme.

Keywords

References

  1. Y. Kanayama, Y. Kimura, F. Miyazaki, and T. Noguchi, "A stable tracking control method for an autonomous mobile robot," Proc. of the IEEE International Conference on Robotics and Automation, USA, vol. 1, pp. 384-389, 13-18 May 1990.
  2. D. H. Kim and J. H. Oh, "Tracking control of a two-wheeled mobile robot using input-output linearization," Control Engineering Practice, vol. 7, no. 3, pp. 369-373, Mar. 1999. https://doi.org/10.1016/S0967-0661(98)00184-1
  3. Y. Hu and S. X. Yang, "A fuzzy neural dynamics based tracking controller for a nonholonomic mobile robot," Proc. of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, vol. 1, pp. 205-210, Jul. 2003.
  4. R. Fierro and F. L. Lewis, "Control of a nonholonomic mobile robot: backstepping kinematics into dynamics," Proc. of the IEEE International Conference on Decision and Control, USA, vol. 4, pp. 3805-3810, Dec. 1995.
  5. M. S. Kim, J.-H. Shin, S. G. Hong, and J. J. Lee, "Designing a robust adaptive dynamic controller for nonholonomic mobile robots under modeling uncertainty and disturbances," Mechatronics, vol. 13, no. 5, pp. 507-519, Jun. 2003. https://doi.org/10.1016/S0957-4158(02)00002-8
  6. R. Fierro and F. L. Lewis, "Control of a nonholonomic mobile robot using neural networks," IEEE Trans. on Neural Networks, vol. 9, no. 4, pp. 589-600, Jul. 1998. https://doi.org/10.1109/72.701173
  7. T. Fukao, H. Nakagawa, and N. Adachi, "Adaptive tracking control of a nonholonomic mobile robots," IEEE Trans. on Robotics and Automation, vol. 16, no. 5, pp. 609-615, Oct. 2000. https://doi.org/10.1109/70.880812
  8. S. Kim, K. Seo, and Y. Cho, "A trajectory tracking control of wheeled mobile robot using a model reference adaptive fuzzy controller," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 15, no. 7, pp. 711-719, Jul. 2009. https://doi.org/10.5302/J.ICROS.2009.15.7.711
  9. N. A. Martins, D. W. Bertol, D. R. De Pieri, and E. B. Castelan, "Neural dynamic control of a nonholonomic mobile robot incorporating the actuator dynamics," Proc. of the International Conference on Computational Intelligence for Modelling Control & Automation, pp. 563-568, Dec. 2008.
  10. T. Das and I. N. Kar, "Design and implementation of an adaptive fuzzy logic-based controller for wheeled mobile robots," IEEE Trans. on Control System Technology, vol. 14, no. 3, pp. 501- 510, May 2006. https://doi.org/10.1109/TCST.2006.872536
  11. N. H. Giap, J.-H. Shin, and W.-H. Kim, "Adaptive robust fuzzy control for path tracking of a wheeled mobile robot," Artificial Life and Robotics, vol. 13, no. 1, pp. 134-138, Dec. 2008. https://doi.org/10.1007/s10015-008-0519-3
  12. J.-H. Shin, W.-H. Kim, and M.-N. Lee, "Robust adaptive fuzzy tracking control using a FBFN for a mobile robot with actuator dynamics," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 16, no. 4, pp. 319-328, Apr. 2010. https://doi.org/10.5302/J.ICROS.2010.16.4.319
  13. Z.-G. Hou, A.-M. Zou, L. Cheng, and M. Tan, "Adaptive control of an electrically driven nonholonomic mobile robot via backstepping and fuzzy approach," IEEE Trans. on Control System Technology, vol. 17, no. 4, pp. 803-815, Jul. 2009. https://doi.org/10.1109/TCST.2009.2012516
  14. B. S. Park, S. J. Yoo, J. B. Park, and Y. H. Choi, "A simple adaptive control approach for trajectory tracking of electrically driven nonholonomic mobile robots," IEEE Trans. on Control System Technology, vol. 18, no. 5, pp. 1199-1206, Sep. 2010. https://doi.org/10.1109/TCST.2009.2034639
  15. B. S. Park, S. J. Yoo, J. B. Park, and Y. H. Choi, "Adaptive output-feedback control for trajectory tracking of electrically driven non-holonomic mobile robots," IET Control Theory and Applications, vol. 5, no. 6, pp. 830-838, Apr. 2011. https://doi.org/10.1049/iet-cta.2010.0219
  16. K. Shojaei and A. M. Shahri, "Output feedback tracking control of uncertain non-holonomic wheeled mobile robots: A dynamic surface control approach," IET Control Theory and Applications, vol. 6, no. 2, pp. 216-228, Jan. 2012. https://doi.org/10.1049/iet-cta.2011.0169
  17. L. -X. Wang and J. M. Mendel, "Fuzzy basis functions, universal approximation, and orthogonal least-squares learning," IEEE Trans. on Neural Networks, vol. 3, no. 5, pp. 807-814, Sep. 1992. https://doi.org/10.1109/72.159070
  18. L. -X. Wang, "Fuzzy systems are universal approximators," Proc. of the IEEE International Conference on Fuzzy Systems, USA, pp. 1163-1170, Mar. 1992.

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