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Adaptive fuzzy visual tracking control for manipulator with quantized saturation input

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Abstract

This paper investigates the adaptive fuzzy visual tracking control problem for telecontrolled manipulator system with input quantized by the proposed saturation switch quantizer (SSQ). Compared with the existing logarithmic quantizer and uniform quantizer, the major superiority of this newly SSQ lies in its adjustable communication rate and quantization density, simultaneously taking the input saturation effect into account. By establishing a nonlinear decomposition-based scheme for the output of SSQ, the control difficulty caused by discrete quantized input is overcome successfully. In addition, the requirement of visual velocity in controller construction is removed by introducing a visual velocity observer, and thus, large image noises and computational burden are both avoided. Subsequently, without the exact knowledge of robot dynamics, a novel adaptive fuzzy visual servoing controller is developed to guarantee the boundedness of closed-loop signals and the tracking performance. The effectiveness of the proposed adaptive fuzzy control scheme is confirmed by comparative simulations.

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References

  1. Asl, H.J., Yoon, J.: Robust image-based control of the quadrotor unmanned aerial vehicle. Nonlinear Dyn. 85(3), 2035–2048 (2016)

    Article  MATH  Google Scholar 

  2. Tsai, C.-Y., Song, K.-T.: Visual tracking control of a wheeled mobile robot with system model and velocity quantization robustness. IEEE Trans. Control Syst. Technol. 17(3), 520–527 (2009)

    Article  Google Scholar 

  3. Siradjuddin, I., Behera, L., McGinnity, T.M., Coleman, S.: Image-based visual servoing of a 7-DOF robot manipulator using an adaptive distributed fuzzy PD controller. IEEE/ASME Trans. Mechatron. 19(2), 512–523 (2014)

    Article  Google Scholar 

  4. Liu, Y.-H., Wang, H., Wang, C., Lam, K.K.: Uncalibrated visual servoing of robots using a depth-independent interaction matrix. IEEE Trans. Robot. 22(4), 804–817 (2006)

    Article  Google Scholar 

  5. Wang, H., Liu, Y.H., Chen, W., Wang, Z.: A new approach to dynamic eye-in-hand visual tracking using nonlinear observers. IEEE/ASME Trans. Mechatron. 16(2), 387–394 (2011)

    Article  Google Scholar 

  6. Lippiello, V., Siciliano, B., Villani, L.: Position-based visual servoing in industrial multirobot cells using a hybrid camera configuration. IEEE Trans. Robot. 23(1), 73–86 (2007)

    Article  Google Scholar 

  7. Janabi-Sharifi, F., Marey, M.: A Kalman-filter-based method for pose estimation in visual servoing. IEEE Trans. Robot. 26(5), 939–947 (2010)

    Article  Google Scholar 

  8. Baumann, M., Léonard, S., Croft, E.A., Little, J.J.: Path planning for improved visibility using a probabilistic road map. IEEE Trans. Robot. 26(1), 195–200 (2010)

    Article  Google Scholar 

  9. Chan, C.S., Liu, H.: Fuzzy qualitative human motion analysis. IEEE Trans. Fuzzy Syst. 17(4), 851–862 (2009)

    Article  Google Scholar 

  10. Petruska, A.J., Mahoney, A.W., Abbott, J.J.: Remote manipulation with a stationary computer-controlled magnetic dipole source. IEEE Trans. Robot. 30(5), 1222–1227 (2014)

    Article  Google Scholar 

  11. Song, G., Li, T., Hu, K., Zheng, B.-C.: Observer-based quantized control of nonlinear systems with input saturation. Nonlinear Dyn. 86(2), 1157–1169 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  12. Song, G., Li, T., Li, Y., Lu, J.: Quantized output feedback stabilization for nonlinear discrete-time systems subject to saturating actuator. Nonlinear Dyn. 83(1–2), 305–317 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  13. Alink, M.S.O., Kokkeler, A.B.J., Klumperink, E.A.M., Rovers, K.C., Smit, G.J.M., Nauta, B.: Spurious-free dynamic range of a uniform quantizer. IEEE Trans. Circuits Syst. II Express Briefs 56(6), 434–438 (2009)

    Article  Google Scholar 

  14. Na, S., Neuhoff, D.L.: Asymptotic MSE distortion of mismatched uniform scalar quantization. IEEE Trans. Inf. Theory 58(5), 3169–3181 (2012)

    Article  MathSciNet  Google Scholar 

  15. Zhang, H., Yan, H., Yang, F., Chen, Q.: Quantized control design for impulsive fuzzy networked systems. IEEE Trans. Fuzzy Syst. 19(6), 1153–1162 (2011)

    Article  Google Scholar 

  16. Lu, R., Cheng, H., Bai, J.: Fuzzy-model-based quantized guaranteed cost control of nonlinear networked systems. IEEE Trans. Fuzzy Syst. 23(3), 567–575 (2015)

    Article  Google Scholar 

  17. Xing, L., Wen, C., Su, H., Liu, Z., Cai, J.: Robust control for a class of uncertain nonlinear systems with input quantization: Robust control with input quantization. Int. J. Robust Nonlinear Control 26(8), 1585–1596 (2016)

    Article  MATH  Google Scholar 

  18. Xing, L., Wen, C., Su, H., Cai, J., Wang, L.: A new adaptive control scheme for uncertain nonlinear systems with quantized input signal. J. Frankl. Inst. 352(12), 5599–5610 (2015)

    Article  MathSciNet  Google Scholar 

  19. Ceragioli, F., De Persis, C., Frasca, P.: Discontinuities and hysteresis in quantized average consensus. Automatica 47(9), 1916–1928 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  20. Zhou, J., Wen, C., Yang, G.: Adaptive backstepping stabilization of nonlinear uncertain systems with quantized input signal. IEEE Trans. Autom. Control 59(2), 460–464 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  21. Xing, L., Wen, C., Zhu, Y., Su, H., Liu, Z.: Output feedback control for uncertain nonlinear systems with input quantization. Automatica 65, 191–202 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  22. Wang, H., Liu, Y.H., Zhou, D.: Dynamic visual tracking for manipulators using an uncalibrated fixed camera. IEEE Trans. Robot. 23(3), 610–617 (2007)

    Article  Google Scholar 

  23. Cheah, C.C., Hou, S.P., Zhao, Y., Slotine, J.J.E.: Adaptive vision and force tracking control for robots with constraint uncertainty. IEEE/ASME Trans. Mechatron. 15(3), 389–399 (2010)

    Article  Google Scholar 

  24. Zhao, Y., Cheah, C.C.: Neural network control of multifingered robot hands using visual feedback. IEEE Trans. Neural Netw. 20(5), 758–767 (2009)

    Article  Google Scholar 

  25. Zhao, Y., Cheah, C.C.: Vision-based neural network control for constrained robots with constraint uncertainty. IET Control Theory Appl. 2(10), 906–916 (2008)

    Article  MathSciNet  Google Scholar 

  26. Liu, C., Cheah, C.C., Slotine, J.-J.E.: Adaptive Jacobian tracking control of rigid-link electrically driven robots based on visual task-space information. Automatica 42(9), 1491–1501 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  27. Wang, H., Liu, Y.H., Chen, W.: Uncalibrated visual tracking control without visual velocity. IEEE Trans. Control Syst. Technol. 18(6), 1359–1370 (2010)

    Article  Google Scholar 

  28. Wang, L., Meng, B.: Distributed adaptive image-based consensus of networked robotic manipulators without visual velocity measurements. IET Control Theory Appl. 8(18), 2199–2206 (2014)

    Article  MathSciNet  Google Scholar 

  29. Wang, H.: Adaptive visual tracking for robotic systems without image-space velocity measurement. Automatica 55, 294–301 (2015)

  30. Wang, L.-X.: Adaptive Fuzzy Systems and Control: Design and Stability Analysis. Prentice-Hall Inc., Upper Saddle River (1994)

    Google Scholar 

  31. Li, Y., Tong, S., Liu, Y., Li, T.: Adaptive fuzzy robust output feedback control of nonlinear systems with unknown dead zones based on a small-gain approach. IEEE Trans. Fuzzy Syst. 22(1), 164–176 (2014)

    Article  Google Scholar 

  32. Zhang, T.P., Ge, S.S.: Adaptive neural control of MIMO nonlinear state time-varying delay systems with unknown dead-zones and gain signs. Automatica 43(6), 1021–1033 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  33. Chen, C., Liu, Z., Zhang, Y., Chen, C.L.P.: Adaptive control of robotic systems with unknown actuator nonlinearities and control directions. Nonlinear Dyn. 81(3), 1289–1300 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  34. He, W., He, X., Ge, S.S.: Boundary output feedback control of a flexible string system with input saturation. Nonlinear Dyn. 80(1–2), 871–888 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  35. Gueaieb, W., Al-Sharhan, S., Bolic, M.: Robust computationally efficient control of cooperative closed-chain manipulators with uncertain dynamics. Automatica 43(5), 842–851 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  36. Gueaieb, W., Karray, F., Al-Sharhan, S.: A robust hybrid intelligent position/force control scheme for cooperative manipulators. IEEE/ASME Trans. Mechatron. 12(2), 109–125 (2007)

    Article  Google Scholar 

  37. Spong, M.W., Vidyasagar, M.: Robot Dynamics and Control. Wiley, New York (2008)

    Google Scholar 

  38. Lafmejani, H.S., Zarabadipour, H.: Modeling, simulation and position control of 3DOF articulated manipulator. Indones. J. Electr. Eng. Inform. 2(3), 132–140 (2014)

    Google Scholar 

  39. Shen, H., Zhu, Y., Zhang, L., Park, J.H.: Extended dissipative state estimation for Markov jump neural networks with unreliable links. IEEE Trans. Neural Netw. Learn. Syst. (2016, in press)

  40. Shen, H., Wu, Z.-G., Park, J.H.: Reliable mixed passive and \(H_\infty \) filtering for semi-Markov jump systems with randomly occurring uncertainties and sensor failures. Int. J. Robust. Nonlinear Control 25(17), 3231–3251 (2015)

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Faculty of Automation, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China

    Fujie Wang, Zhi Liu & Yun Zhang

  2. Faculty of Science and Technology, University of Macau, Macau, China

    C. L. Philip Chen

Authors
  1. Fujie Wang
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  2. Zhi Liu
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  3. Yun Zhang
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  4. C. L. Philip Chen
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Corresponding author

Correspondence to Zhi Liu.

Additional information

This work was supported in part by the National Natural Science Foundation of China under Grant 61573108, in part by National Natural Science Foundation of China (U1501251), in part by the Natural Science Foundation of Guangdong Province 2016A030313715, in part by the Natural Science Foundation of Guangdong Province through the Science Fund for Distinguished Young Scholars under Grant S20120011437 and in part by the Ministry of Education of New Century Excellent Talent under Grant NCET–12–0637 .

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Wang, F., Liu, Z., Zhang, Y. et al. Adaptive fuzzy visual tracking control for manipulator with quantized saturation input. Nonlinear Dyn 89, 1241–1258 (2017). https://doi.org/10.1007/s11071-017-3513-2

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  • DOI: https://doi.org/10.1007/s11071-017-3513-2

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