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Journal of Mechanical Science and Technology

, Volume 32, Issue 12, pp 5897–5906 | Cite as

Adaptive finite-time backstepping control for a two-wheeled mobile manipulator

  • Yudong Zhao
  • Shikai Zhang
  • Jangmyung LeeEmail author
Article
  • 17 Downloads

Abstract

This study presents a dynamic modeling and adaptive finite-time backstepping control (AFBSC) strategy for a two-wheeled mobile platform with a three-link manipulator. The Euler-Lagrange method, partially combined with the Newton method, produces a simplified dynamic model wherein the complex coordination transformation process used in traditional mobile platform modeling processes is not required to be considered. Thus, the simplified two-wheeled mobile manipulator achieves fast locomotion and flexible manipulation in the given workspace. Finite-time backstepping virtual errors are introduced into the recursive design procedures to guarantee the rapid convergence of control performance. Furthermore, the uncertainties of coupled nonlinear dynamics are compensated by an adaptive error compensator. Comparative simulations and experiments with an adaptive finite-time sliding mode control (AFSMC) demonstrate the effectiveness of the proposed control scheme. The settling time and variance of tracking error are selected as the criteria that can reflect convergence speed and stability, respectively, under AFBSC and AFSMC, to analyze the experimental data.

Keywords

Adaptive control Backstepping control Lyapunov stability theory Two-wheeled mobile manipulator 

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References

  1. [1]
    C. P. Tang, P. T. Miller and V. N. Krovi, Kinematic control of a nonholonomic wheeled mobile manipulator–a differential flatness approach, Proc. of DSCC 2008 ASME Dynamic System and Control Conference, Ann Arbor, Michigan, USA (2008) 1117–1124.CrossRefGoogle Scholar
  2. [2]
    J. H. Chung and S. A. Velinsky, Robust interaction control of a mobile manipulator–dynamic model based coordination, Journal of Intelligent and Robotic System, 26 (1) (1999) 47–63.CrossRefGoogle Scholar
  3. [3]
    S. Lin and A. A. Goldenberg, Neural–network control of mobile manipulators, IEEE Transactions on Neural Networks, 12 (5) (2001) 1121–1133.CrossRefGoogle Scholar
  4. [4]
    C. C. Tsai, M. B. Cheng and S. C. Lin, Dynamic modeling and tracking control of a nonholonomic wheeled mobile manipulator with dual arms, Journal of Intelligent and Robotic Systems, 47 (4) (2006) 317–340.CrossRefGoogle Scholar
  5. [5]
    R. Rastegari and K. Alipour, Control of wheeled mobile manipulator with flexible suspension considering wheels slip effects, Journal of Computer & Robotics, 10 (2) (2017) 77–85.Google Scholar
  6. [6]
    M. B. Cheng, W. C. Su and C. C. Tsai, Robust tracking control of a unicycle–type wheeled mobile manipulator using a hybrid sliding mode fuzzy neural network, International Journal of Systems Science, 43 (3) (2012) 408–425.MathSciNetCrossRefzbMATHGoogle Scholar
  7. [7]
    Z. Li, C. Yang and Y. Tang, Decentralized adaptive fuzzy control of coordinated multiple mobile manipulators interacting with non–rigid environments, IET Control Theory and Application, 7 (3) (2013) 397–410.CrossRefGoogle Scholar
  8. [8]
    F. Grasser, A. Amigo and S. Colombi, JOE: A mobile, inverted pendulum, IEEE Transactions on Industrial Electronics, 49 (1) (2002) 107–114.CrossRefGoogle Scholar
  9. [9]
    C. H. Chiu, Y. W. Lin and C. H. Lin, Real–time control of a wheeled inverted pendulum based on an intelligent model free controller, Mechatronics, 21 (3) (2011) 523–533.MathSciNetCrossRefGoogle Scholar
  10. [10]
    S. S. Lin, C. C. Tsai and H. C. Huang, Adaptive robust selfbalancing and steering of a two–wheeled human transportation vehicle, Journal of Intelligent & Robotic Systems, 62 (1) (2011) 103–123.CrossRefzbMATHGoogle Scholar
  11. [11]
    H. G. Lee and S. Jung, Balancing and navigation control of a mobile inverted pendulum robot using sensor fusion of low cost sensors, Mechatronics, 22 (1) (2012) 95–105.CrossRefGoogle Scholar
  12. [12]
    P. K. W. Abeygunawardhana and T. Murakami, Vibration suppression of two–wheeled mobile manipulator using resonance–ratio–control–based null–space control, IEEE Transactions on Industrial Electronics, 57 (12) (2010) 4137–4146.CrossRefGoogle Scholar
  13. [13]
    S. Ahmad, N. H. Siddique and M. O. Tokhi, A modular fuzzy control approach for two–wheeled wheelchair, Journal of Intelligent & Robotic Systems, 64 (3–4) (2011) 401–426.CrossRefGoogle Scholar
  14. [14]
    K. Kristic, I. Kanellakopoulos and P. V. Kokotovic, Non–linear and adaptive control design, John Wiley & Sons, New York, USA (1995).Google Scholar
  15. [15]
    C. W. Chung and Y. Chang, Backstepping control of multiinput non–linear systems, IET Control Theory and Appl., 7 (14) (2013) 1773–1779.MathSciNetCrossRefGoogle Scholar
  16. [16]
    S. I. Han and J. M. Lee, Improved prescribed performance constraint control for a strict feedback non–linear dynamic system, IET Control Theory and Appl., 7 (14) (2013)1818–1827.Google Scholar
  17. [17]
    A. V. Gulalkari, P. S. Pratama and G. Hoang, Object tracking and following six–legged robot system using Kinect camera based on Kalman filter and backstepping controller, Journal of Mechanical Science and Technology, 29 (12) (2015) 5425–5436.CrossRefGoogle Scholar
  18. [18]
    Y. S. Lu, H. H. Chiu and S. F. Lien, An improved backstepping design for the control of an underactuated inverted pendulum, Journal of Mechanical Science and Technology, 27 (3) (2013) 865–873.CrossRefGoogle Scholar
  19. [19]
    J. Cai, C. Wen and H. Su, Adaptive backstepping control for a class of nonlinear systems with non–triangular structural uncertainties, IEEE Transactions on Automatic Control, 62 (10) (2017) 5220–5226.MathSciNetCrossRefzbMATHGoogle Scholar
  20. [20]
    J. Yao, Z. Jiao and B. Yao, Nonlinear adaptive robust backstepping force control of hydraulic load simulator: Theory and experiments, Journal of Mechanical Science and Technology, 28 (4) (2014) 1499–1507.MathSciNetCrossRefGoogle Scholar
  21. [21]
    R. Wai, J. Yao and J. Lee, Backstepping fuzzy–neuralnetwork control design for hybrid maglev transportation system, IEEE Transactions on Neural Networks and Learning Systems, 26 (2) (2015) 302–317.MathSciNetCrossRefGoogle Scholar
  22. [22]
    J. Fei, Y. Chu and S. Hou, A backstepping neural global sliding mode control using fuzzy approximator for threephase active power filter, Digital Object Identifier, 10 (1109) (2017) 16021–16032.Google Scholar
  23. [23]
    L. Huang, Y. Li and S. Tong, Command filter–based adaptive fuzzy backstepping control for a class of switched nonlinear systems with input quantization, IET The Institution of Engineering And Technology, 11 (12) (2017) 1948–1958.Google Scholar

Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics EngineeringPusan National University, Jangjeon-dong, Geumjeong-guBusanKorea

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