JACIII Vol.20 No.5 pp. 828-835
doi: 10.20965/jaciii.2016.p0828


Network Teleoperation Robot System Control Based on Fuzzy Sliding Mode

Zhongda Tian*, Xianwen Gao**, and Peiqin Guo**

*College of Information Science and Engineering, Shenyang University of Technology
Shenyang 110870, China
**College of Information Science and Engineering, Northeastern University
Shenyang 110819, China

March 9, 2016
July 28, 2016
Online released:
September 20, 2016
September 20, 2016
teleoperation, robot, delay, sliding mode, fuzzy control

A teleoperation robot system is connected through a network. However, stochastic delay in such a network can affect its performance, or even make the system unstable. To solve this problem, this paper proposes a teleoperation robot system control method based on fuzzy sliding mode. In the proposed method, a delay generator generates variable delay conforming to a shift gamma distribution designed to simulate actual network delay. In addition, a proposed fuzzy sliding mode controller based on switching gain adjustment is used to rectify the chattering phenomenon in the sliding mode controller of the teleoperation robot system. In the controller, the master hand uses impedance control and realizes feedback from the slave hand. Controller simulation comparison results show that the proposed fuzzy sliding mode controller effectively eliminates the sliding mode control chattering phenomenon as the slave hand stabilizes the tracking velocity of the master hand. Consequently, the system exhibits improved dynamic performance.

  1. [1] J. H. Chen, X. H. Mu, F. P. Du, and H. L. Gao, “Key technologies of telepresence in time-delayed teleoperation system: Review and analysis,” 2013 3rd Int. Conf. on Mechanical Science and Engineering, pp. 48-55, 2013.
  2. [2] B. Aude and R. Stephane, “A review of haptic feedback teleoperation systems for micromanipulation and microassembly,” IEEE Trans. on Automation Science and Engineering, Vol.10, pp. 496-502, 2013.
  3. [3] M. Bowthorpe, M. Tavakoli, and H. Becher, “Smith predictor-based robot control for ultrasound-guided teleoperated beating-heart surgery,” IEEE J. of Biomedical and Health Informatics, Vol.18, pp. 157-166, 2013.
  4. [4] R. Q. Wang, C. J. Xia, W. J. Gu, and K. H. Li, “Fuzzy singularly perturbed model and stability analysis of bilateral teleoperation system,” Proc. of the 30th Chinese Control Conf., pp. 3664-3668, 2011.
  5. [5] P. G. Griffiths and A. M. Okamura, “Defining performance tradeoffs for multi-degree-of-freedom bilateral teleoperators with LQG control,” Proc. of the IEEE Conf. on Decision and Control, pp. 3542-3547, 2010.
  6. [6] A. Pallegedara, Y. Matsuda, and N. Egashira, “Experimental evaluation of teleoperation system with force-free control and visual servo control by human operator perception,” Artificial Life and Robotics, Vol.17, pp. 388-394, 2013.
  7. [7] Y. Ning, H. H. Wang, and L. L. Han, “Force feedback predictive control based on BP neural network of MIS robot,” Int. Conf. on Electric Information and Control Engineering, pp. 419-422, 2011.
  8. [8] S. X. Yang and M. Meng, “An efficient neural network model for path planning of car-like robots in dynamic environment,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.4, pp. 220-229, 2000.
  9. [9] Y. H. Yang, F. P. Yang, J. N. Hua, and H. Y. Li, “Generalized predictive control for space teleoperation systems with long time-varying delays,” Proc. 2012 IEEE Int. Conf. on Systems, Man, and Cybernetics, pp. 3057-3062, 2012.
  10. [10] B. Zhang, G. J. Tang, and H. Y. Li, “Predictive Control of Teleoperation Rendezvous with Large Time Delay,” 10th IEEE Int. Conf. on Control and Automation, pp. 896-901, 2013.
  11. [11] A. Hace, M. Franc, and K. Jezernik, “Sliding mode control for scaled bilateral teleoperation,” 37th Annual Conf. of the IEEE Industrial Electronics Society, pp. 3430-3435, 2011.
  12. [12] A. Jafari and J. H. Ryu, “Transparency improved sliding-mode control design for bilateral teleoperation systems by using virtual manipulator concept,” 3rd IFAC Symp. on Telematics Applications, pp. 21-26, 2013.
  13. [13] Z. Nowacki, D. Owczarz, and P. Wozniak, “On the robustness of fuzzy control of an overhead crane,” Proc. of the IEEE Int. Symp. on Industrial Electronics, pp. 433-437, 1996.
  14. [14] W. J. Wang and H. R. Lin, “Fuzzy control design for the trajectory tracking on uncertain nonlinear systems,” IEEE Trans. on Fuzzy Systems, Vol.7, pp. 53-62, 1999.
  15. [15] X. N. Wang, “Resource-aware clustering based AODVjr routing protocol in the Internet of things,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.17, pp. 622-627, 2013.
  16. [16] D. Kim, J. Y. Lee, and D. K. Sung, “A shifted gamma distribution model for long-range dependent internet traffic,” IEEE Communications Letters, Vol.7, pp. 124-126, 2008.
  17. [17] J. P. Zhao and X. W. Gao, “Time-delay analysis and estimation of Internet-based robot teleoperation system,” 21st Chinse Control and Decision Conf., pp. 4643-4646, 2009.
  18. [18] W. J. Wang, “Scale parameter of gamma distribution and its auto-covariance estimation,” Chinese J. of Applied Probability, Vol.3, pp. 193-202, 1987.
  19. [19] H. Han and Y. Higaki, “Controller designs for a class of Polynomial fuzzy models,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.19, pp. 796-803, 2015.
  20. [20] H. Han and H. K. Lam, “Discrete sliding-mode control for a class of T-S fuzzy models with modeling error,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.18, pp. 908-117, 2014.
  21. [21] T. L. Shen, “Fundamentals of robust control for robots,” Beijing: Tsinghua University Press, 2004.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Mar. 22, 2017