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Journal of Systems Science and Complexity

, Volume 26, Issue 6, pp 940–956 | Cite as

Finite-time tracking control for motor servo systems with unknown dead-zones

  • Qiang ChenEmail author
  • Li Yu
  • Yurong Nan
Article

Abstract

A finite-time tracking control scheme is proposed in this paper based on the terminal sliding mode principle for motor servo systems with unknown nonlinear dead-zone inputs. By using the differential mean value theorem, the dead-zone is represented as a time-varying system and thus the inverse compensation approach is avoided. Then, an indirect terminal sliding mode control (ITSMC) is developed to guarantee the finite-time convergence of the tracking error and to overcome the singularity problem in the traditional terminal sliding mode control. In the proposed controller design, the unknown nonlinearity of the system is approximated by a simple sigmoid neural network, and the approximation error is diminished by employing a robust term. Comparative experiments on a turntable servo system are conducted to show the superior performance of the proposed method.

Key words

Dead zone finite-time control neural network servo system 

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References

  1. [1]
    Naso D, Cupertino F, and Turchiano B, Precise position control of tubular linear motors with neural networks and composite learning, Control Engineering Practice, 2010, 18(5): 515–522.CrossRefGoogle Scholar
  2. [2]
    Tao G and Kokotovic P V, Adaptive control of plants with unknown deadzones, IEEE Transactions on Automatic Control, 1994, 39(1): 59–68.MathSciNetCrossRefzbMATHGoogle Scholar
  3. [3]
    Wang X S, Su C Y, and Hong H, Robust adaptive control of a class of linear systems with unknown dead-zone, Automatica, 2004, 40(3): 407–413.MathSciNetCrossRefzbMATHGoogle Scholar
  4. [4]
    Hua C C, Wang Q G, and Guan X P, Adaptive tracking controller design of nonlinear systems with time delays and unknown dead-zone input, IEEE Transactions on Automatic Control, 2008, 53(7): 1753–1759.MathSciNetCrossRefGoogle Scholar
  5. [5]
    Ibrir S, Xie W F, and Su C Y, Adaptive tracking of nonlinear systems with non-symmetric deadzone input, Automatica, 2007, 43(3): 522–530.MathSciNetCrossRefzbMATHGoogle Scholar
  6. [6]
    Zhang T P and Ge S S, Adaptive neural network tracking control of MIMO nonlinear systems with unknown dead zones and control directions, IEEE Transactions on Neural Network, 2009, 20(3): 483–497.MathSciNetCrossRefGoogle Scholar
  7. [7]
    Na J, Ren X M, Herrmann G, and Qiao Z, Adaptive neural dynamic surface control for servo systems with unknown dead-zone, Control Engineering Practice, 2011, 19(11): 1328–1343.CrossRefGoogle Scholar
  8. [8]
    Imura J, Sugie T, and Yoshikawa T, Adaptive robust control of robot manipulators — Theory and experiment, IEEE Transactions on Robotics and Automation, 1994, 10(5): 705–710.MathSciNetCrossRefGoogle Scholar
  9. [9]
    Xu L and Yao B, Adaptive robust precision motion control of linear motors with negligible electrical dynamics: Theory and experiments, IEEE Transactions on Mechatronics, 2001, 6(4): 444–452.CrossRefGoogle Scholar
  10. [10]
    Man Z H and Yu X H, Adaptive terminal sliding mode tracking control for rigid robotic manipulators with uncertain dynamics, JSME International Journal of Mechanical Systems, Machine Elements, and Manufacturing, 1997, 40(3): 493–502.CrossRefGoogle Scholar
  11. [11]
    Feng Y, Yu X H, and Man Z H, Non-singular terminal slidng mode control of rigid manipulators, Automatica, 2002, 38(12): 2159–2167.MathSciNetCrossRefzbMATHGoogle Scholar
  12. [12]
    Yu S, Yu X H, and Shirinzadehc B, Continuous finite-time control for robotic manipulators with terminal sliding mode, Automatica, 2005, 41(11): 1957–1964.MathSciNetCrossRefzbMATHGoogle Scholar
  13. [13]
    Liu Y L, Ji Y F, Cheng Z, and Jia T, Speed and rotor position identification for PMSM based on improved nonsingular terminal sliding-mode, Advanced Materials Research, 2012, 424–425: 796–800.Google Scholar
  14. [14]
    Zheng X M, Li Q M, Wang W, and Feng Y, Higher-order nonsingular terminal sliding mode deadtime compensation method in PMSM, 37th Annual Conference on IEEE Industrial Electronics Society, Melbourne, November 2011, 592–597.Google Scholar
  15. [15]
    Li S H, Zhou M M, and Yu X H, Design and implementation of terminal sliding mode control method for PMSM speed regulation system, IEEE Transactions on Industrial Informatics, 10.1109/TII.2012.2226896, published online, November 2012.Google Scholar
  16. [16]
    Li S H, Liu H X, and Ding S H, A speed control for A PMSM using finite-time feedback control and disturbance compensation, Transactions of the Institute of Measurement and Control, 2010, 32(2): 170–187.CrossRefGoogle Scholar
  17. [17]
    Wang L Y, Chai T Y, and Zhai L F, Neural-network-based terminal sliding-mode control of robotic manipulators including actuator dynamics, IEEE Transactions on Industrial Electronics, 2009, 56(9): 3296–3304.CrossRefGoogle Scholar
  18. [18]
    Zhao D Y, Li S Y, and Gao F, A new terminal sliding mode control for robotic manipulators, International Journal of Control, 2009, 82(10): 1804–1813.MathSciNetCrossRefzbMATHGoogle Scholar
  19. [19]
    Na J, Ren X M, and Gao Y, et al. Adaptive neural network state predictor and tracking control for nonlinear time-delay systems, International Journal of Innovative Computing, Information, and Control, 2010, 6(2): 627–639.Google Scholar
  20. [20]
    Ren X M and Rad A B, Identification of nonlinear systems with unknown time delay based on time-delay neural networks, IEEE Transactions on Neural Networks, 2007, 18(5): 1536–1541.CrossRefGoogle Scholar
  21. [21]
    Ren X M, Lewis F L, and Zhang J L, Neural network compensation control for mechanical systems with disturbances, Automatica, 2009, 45(5): 1221–1226.MathSciNetCrossRefzbMATHGoogle Scholar
  22. [22]
    Na J, Ren X M, Shang C, and Guo Y, Adaptive neural network predictive control for nonlinear pure feedback systems with input delay, Journal of Press Control, 2012, 22(1): 194–206.CrossRefGoogle Scholar
  23. [23]
    Zou A M, Kumar K D, Hou Z G, and Liu X, Finite-time attitude tracking control for spacecraft using terminal sliding mode and chebyshev neural network, IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 2011, 41(4): 950–963.CrossRefGoogle Scholar
  24. [24]
    Chen Q, Ren X M, and Oliver J A, Identifier-based adaptive neural dynamic surface control for uncertain DC-DC buck converter system with input constraint, Communications in Nonlinear Science and Numerical Simulation, 2012, 17(4): 1871–1883.MathSciNetCrossRefzbMATHGoogle Scholar
  25. [25]
    Hong Y, Huang J, and Xu Y, On an output finite-time stabilization problem, IEEE Transactions on Automatic Control, 2001, 46(2): 305–309.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.College of Information EngineeringZhejiang University of TechnologyHangzhouChina

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