Volume 20, Issue 1
  • ISSN 1572-0373
  • E-ISSN: 1572-0381
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A decentralized adaptive control based on human linguistic is investigated to learn human behaviors for multiple robotic manipulators. Many experts’ words or sentences can be transferred into the control actions by employing membership functions in robot systems, which can be synthesized fuzzy controller by employing reasoning mechanism. For the unknown model dynamical robot manipulators, one adjustable parameter that relates to the approximation accuracy of fuzzy logic systems is introduced at first, which be utilized to deal with the unknown dynamics of robot manipulators. Switching fuzzy adaptive controller is designed to overcome the limitation of logic structure that the number of adaptive laws only focus on fuzzy rules in conventional fuzzy logic systems. Another advantage of this design method is that the control with human linguistic extend the semi-global stability to global stability. Finally, effectiveness of the developed control design scheme has been shown in simulation example.


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  1. Changying, L., T. Shaocheng, L. Yongming, and L. Tieshan
    (2009) “Fuzzy adaptive observer and filter backsteppping control for nonlinear systems,” inAmerican Control Conference, June 10–12, USA, pp.4290–4295.
    [Google Scholar]
  2. Chen, B., X. Liu, K. Liu, and C. Lin
    (2009) “Direct adaptive fuzzy control of nonlinear strict-feedback systems,” Automatica, vol.45, no.6, pp.1530–1535. 10.1016/j.automatica.2009.02.025
    https://doi.org/10.1016/j.automatica.2009.02.025 [Google Scholar]
  3. Chung, S.-J. and J.-J. E. Slotine
    (2009) “Cooperative robot control and concurrent synchronization of lagrangian systems,” IEEE Transactions on Robotics, vol.25, no.3, pp.686–700. 10.1109/TRO.2009.2014125
    https://doi.org/10.1109/TRO.2009.2014125 [Google Scholar]
  4. Hou, Z.-G., L. Cheng, and M. Tan
    (2010) “Multicriteria optimization for coordination of redundant robots using a dual neural network,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol.40, no.4, pp.1075–1087. 10.1109/TSMCB.2009.2034073
    https://doi.org/10.1109/TSMCB.2009.2034073 [Google Scholar]
  5. Hou, Y., S. Tong, and Y. Li
    (2016) “Adaptive fuzzy backstepping control for a class of switched nonlinear systems with actuator faults,” International Journal of Systems Science, vol.47, no.15, pp.3581–3590. 10.1080/00207721.2015.1096428
    https://doi.org/10.1080/00207721.2015.1096428 [Google Scholar]
  6. Hsu, P.
    (1993) “Coordinated control of multiple manipulator systems,” IEEE Transactions on Robotics and Automation, vol.9, no.4, pp.400–410. 10.1109/70.246051
    https://doi.org/10.1109/70.246051 [Google Scholar]
  7. Hsu, Y.-C., G. Chen, and H.-X. Li
    (2001) “A fuzzy adaptive variable structure controller with applications to robot manipulators,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol.31, no.3, pp.331–340. 10.1109/3477.931517
    https://doi.org/10.1109/3477.931517 [Google Scholar]
  8. Huang, J.-T.
    (2012) “Global tracking control of strict-feedback systems using neural networks,” IEEE transactions on neural networks and learning systems, vol.23, no.11, pp.1714–1725. 10.1109/TNNLS.2012.2213305
    https://doi.org/10.1109/TNNLS.2012.2213305 [Google Scholar]
  9. Huynh, V.-N., T. B. Ho, and Y. Nakamori
    (2002) “A parametric representation of linguistic hedges in zadehs fuzzy logic,” International Journal of Approximate Reasoning, vol.30, no.3, pp.203–223. 10.1016/S0888‑613X(02)00075‑0
    https://doi.org/10.1016/S0888-613X(02)00075-0 [Google Scholar]
  10. Kim, E.
    (2004) “Output feedback tracking control of robot manipulators with model uncertainty via adaptive fuzzy logic,” IEEE Transactions on Fuzzy Systems, vol.12, no.3, pp.368–378. 10.1109/TFUZZ.2004.825062
    https://doi.org/10.1109/TFUZZ.2004.825062 [Google Scholar]
  11. Li, S., S. Chen, B. Liu, Y. Li, and Y. Liang
    (2012) “Decentralized kinematic control of a class of collaborative redundant manipulators via recurrent neural networks,” Neurocomputing, vol.91, pp.1–10. 10.1016/j.neucom.2012.01.034
    https://doi.org/10.1016/j.neucom.2012.01.034 [Google Scholar]
  12. Li, Z., C. Yang, and Y. Tang
    (2013) “Decentralised adaptive fuzzy control of coordinated multiple mobile manipulators interacting with non-rigid environments,” IET Control Theory & Applications, vol.7, no.3, pp.397–410. 10.1049/iet‑cta.2011.0334
    https://doi.org/10.1049/iet-cta.2011.0334 [Google Scholar]
  13. Li, L., Z. Zhang, and J. Xu
    (2014) “A generalized nonlinear h filter design for discrete-time lipschitz descriptor systems,” Nonlinear Analysis: Real World Applications, vol.15, pp.1–11. 10.1016/j.nonrwa.2013.04.003
    https://doi.org/10.1016/j.nonrwa.2013.04.003 [Google Scholar]
  14. Li, Z., C. Yang, C.-Y. Su, S. Deng, F. Sun, and W. Zhang
    (2015) “Decentralized fuzzy control of multiple cooperating robotic manipulators with impedance interaction,” IEEE Transactions on Fuzzy Systems, vol.23, no.4, pp.1044–1056. 10.1109/TFUZZ.2014.2337932
    https://doi.org/10.1109/TFUZZ.2014.2337932 [Google Scholar]
  15. Liu, Y.-J., Y. Gao, S. Tong, and Y. Li
    (2016) “Fuzzy approximation-based adaptive backstepping optimal control for a class of nonlinear discrete-time systems with dead-zone,” IEEE Transactions on Fuzzy Systems, vol.24, no.1, pp.16–28. 10.1109/TFUZZ.2015.2418000
    https://doi.org/10.1109/TFUZZ.2015.2418000 [Google Scholar]
  16. Liu, Y.-J., S. Tong, D.-J. Li, and Y. Gao
    (2016) “Fuzzy adaptive control with state observer for a class of nonlinear discrete-time systems with input constraint,” IEEE Transactions on Fuzzy Systems, vol.24, no.5, pp.1147–1158. 10.1109/TFUZZ.2015.2505088
    https://doi.org/10.1109/TFUZZ.2015.2505088 [Google Scholar]
  17. Liu, Y.-J., S. Lu, and S. Tong
    (2017) “Neural network controller design for an uncertain robot with time-varying output constraint,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol.47, no.8, pp.2060–2068. 10.1109/TSMC.2016.2606159
    https://doi.org/10.1109/TSMC.2016.2606159 [Google Scholar]
  18. Liu, Z., C. Chen, and Y. Zhang
    (2015) “Decentralized robust fuzzy adaptive control of humanoid robot manipulation with unknown actuator backlash,” IEEE Transactions on Fuzzy Systems, vol.23, no.3, pp.605–616. 10.1109/TFUZZ.2014.2321591
    https://doi.org/10.1109/TFUZZ.2014.2321591 [Google Scholar]
  19. Lixin, W.
    (1997) “A Course in fuzzy system and control.” Prentice Hall, Upper Saddle River, United States.
    [Google Scholar]
  20. Martínez-Rosas, J. C., M. A. Arteaga, and A. M. Castillo-Sánchez
    (2006) “Decentralized control of cooperative robots without velocity-force measurements,” Automatica, vol.42, no.2, pp.329–336. 10.1016/j.automatica.2005.10.007
    https://doi.org/10.1016/j.automatica.2005.10.007 [Google Scholar]
  21. Moosavian, S. A. A. and H. R. Ashtiani
    (2008) “Cooperation of robotic manipulators using non-model-based multiple impedance control,” Industrial Robot: An International Journal, vol.35, no.6, pp.549–558. 10.1108/01439910810909556
    https://doi.org/10.1108/01439910810909556 [Google Scholar]
  22. Novakovic, B. M.
    (1997) “Fuzzy logic robot control synthesis without any rule base,” inAdvanced Robotics, 1997. ICAR’97. Proceedings., 8th International Conference on. IEEE, pp.141–146. 10.1109/ICAR.1997.620174
    https://doi.org/10.1109/ICAR.1997.620174 [Google Scholar]
  23. Silva, C. W. de
    (1995) “Applications of fuzzy logic in the control of robotic manipulators,” Fuzzy Sets and Systems, vol.70, no.2–3, pp.223–234. 10.1016/0165‑0114(94)00219‑W
    https://doi.org/10.1016/0165-0114(94)00219-W [Google Scholar]
  24. Slotine, J.-J. E., W. Li
    (1991) Applied nonlinear control. Prentice hall Englewood Cliffs, NJ.
    [Google Scholar]
  25. Vaščák, J. and L. Madarász
    (2005) “Automatic adaptation of fuzzy controllers,” Acta Polytechnica Hungarica, vol.2, no.2, pp.5–18.
    [Google Scholar]
  26. Wakileh, B. and K. Gill
    (1988) “Use of fuzzy logic in robotics,” Computers in industry, vol.10, no.1, pp.35–46. 10.1016/0166‑3615(88)90046‑2
    https://doi.org/10.1016/0166-3615(88)90046-2 [Google Scholar]
  27. Wang, L.-X. and J. M. Mendel
    (1992) “Fuzzy basis functions, universal approximation, and orthogonal least-squares learning,” IEEE transactions on Neural Networks, vol.3, no.5, pp.807–814. 10.1109/72.159070
    https://doi.org/10.1109/72.159070 [Google Scholar]
  28. Wang, H., Z. Wang, Y.-J. Liu, and S. Tong
    (2017) “Fuzzy tracking adaptive control of discrete-time switched nonlinear systems,” Fuzzy Sets and Systems, vol.316, pp.35–48. 10.1016/j.fss.2016.10.008
    https://doi.org/10.1016/j.fss.2016.10.008 [Google Scholar]
  29. Xu, B., C. Yang, and Y. Pan
    (2015) “Global neural dynamic surface tracking control of strict-feedback systems with application to hypersonic flight vehicle,” IEEE Transactions on Neural Networks and Learning Systems, vol.26, no.10, pp.2563–2575. 10.1109/TNNLS.2015.2456972
    https://doi.org/10.1109/TNNLS.2015.2456972 [Google Scholar]
  30. Yang, Y.
    (2005) “Direct robust adaptive fuzzy control (drafc) for uncertain nonlinear systems using small gain theorem,” Fuzzy Sets and Systems, vol.151, no.1, pp.79–97. 10.1016/j.fss.2004.05.010
    https://doi.org/10.1016/j.fss.2004.05.010 [Google Scholar]
  31. Yang, C., K. Huang, H. Cheng, Y. Li, and C.-Y. Su
    (2017) “Haptic identification by elm-controlled uncertain manipulator,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol.47, no.8, pp.2398–2409. 10.1109/TSMC.2017.2676022
    https://doi.org/10.1109/TSMC.2017.2676022 [Google Scholar]
  32. Yang, C., Y. Jiang, Z. Li, W. He, and C.-Y. Su
    (2017) “Neural control of bimanual robots with guaranteed global stability and motion precision,” IEEE Transactions on Industrial Informatics, vol.13, no.3, pp.1162–1171. 10.1109/TII.2016.2612646
    https://doi.org/10.1109/TII.2016.2612646 [Google Scholar]
  33. Yang, C., J. Luo, Y. Pan, Z. Liu, and C.-Y. Su
    (2018) “Personalized variable gain control with tremor attenuation for robot teleoperation,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol.48, no.10, pp.1759–1770. 10.1109/TSMC.2017.2694020
    https://doi.org/10.1109/TSMC.2017.2694020 [Google Scholar]
  34. Yang, C., X. Wang, L. Cheng, and H. Ma
    (2017) “Neural-learning-based telerobot control with guaranteed performance,” IEEE transactions on cybernetics, vol.47, no.10, pp.3148–3159. 10.1109/TCYB.2016.2573837
    https://doi.org/10.1109/TCYB.2016.2573837 [Google Scholar]
  35. Yang, C., X. Wang, Z. Li, Y. Li, and C.-Y. Su
    (2017) “Teleoperation control based on combination of wave variable and neural networks,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol.47, no.8, pp.2125–2136. 10.1109/TSMC.2016.2615061
    https://doi.org/10.1109/TSMC.2016.2615061 [Google Scholar]
  36. Yang, C., H. Wu, Z. Li, W. He, N. Wang, and C.-Y. Su
    (2018) “Mind control of a robotic arm with visual fusion technology,” IEEE Transactions on Industrial Informatics, vol.14, no.9, pp.3822–3830. 10.1109/TII.2017.2785415
    https://doi.org/10.1109/TII.2017.2785415 [Google Scholar]
  37. Yang, C., C. Zeng, P. Liang, Z. Li, R. Li, and C.-Y. Su
    (2018) “Interface design of a physical human-robot interaction system for human impedance adaptive skill transfer,” IEEE Transactions on Automation Science and Engineering, vol.15, no.1, pp.329–340. 10.1109/TASE.2017.2743000
    https://doi.org/10.1109/TASE.2017.2743000 [Google Scholar]
  38. Yoo, S. J., J. B. Park, and Y. H. Choi
    (2008) “Adaptive output feedback control of flexible-joint robots using neural networks: dynamic surface design approach,” IEEE Transactions on Neural Networks, vol.19, no.10, pp.1712–1726. 10.1109/TNN.2008.2001266
    https://doi.org/10.1109/TNN.2008.2001266 [Google Scholar]
  39. Zadeh, L. A.
    (1975) “The concept of a linguistic variable and its application to approximate reasoningli,” Information sciences, vol.8, no.3, pp.199–249. 10.1016/0020‑0255(75)90036‑5
    https://doi.org/10.1016/0020-0255(75)90036-5 [Google Scholar]
  40. Zhang, W., H. Su, F. Zhu, and D. Yue
    (2012) “A note on observers for discrete-time lipschitz nonlinear systems,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol.59, no.2, pp.123–127. 10.1109/TCSII.2011.2174671
    https://doi.org/10.1109/TCSII.2011.2174671 [Google Scholar]
  41. Zhou, X., X. Liu, A. Jiang, B. Yan, and C. Yang
    (2017) “Improving video segmentation by fusing depth cues and the visual background extractor (vibe) algorithm,” Sensors, vol.17, no.5, p.1177. 10.3390/s17051177
    https://doi.org/10.3390/s17051177 [Google Scholar]
  42. Zhou, Q., H. Li, and P. Shi
    (2015) “Decentralized adaptive fuzzy tracking control for robot finger dynamics,” IEEE Transactions on Fuzzy Systems, vol.23, no.3, pp.501–510. 10.1109/TFUZZ.2014.2315661
    https://doi.org/10.1109/TFUZZ.2014.2315661 [Google Scholar]
  43. Zhou, X., X. Liu, C. Yang, A. Jiang, and B. Yan
    (2017) “Multi-channel features spatio-temporal context learning for visual tracking,” IEEE Access, vol.5, pp.12856–12864. 10.1109/ACCESS.2017.2720746
    https://doi.org/10.1109/ACCESS.2017.2720746 [Google Scholar]
  44. Zhu, W.-H.
    (2005) “On adaptive synchronization control of coordinated multi-robots with flexible/rigid constraints,” IEEE Transactions on Robotics, vol.21, no.3, pp.520–525. 10.1109/TRO.2004.839219
    https://doi.org/10.1109/TRO.2004.839219 [Google Scholar]
  45. Zulfiqar, A., M. Rehan, and M. Abid
    (2016) “Observer design for one-sided lipschitz descriptor systems,” Applied Mathematical Modelling, vol.40, no.3, pp.2301–2311. 10.1016/j.apm.2015.09.056
    https://doi.org/10.1016/j.apm.2015.09.056 [Google Scholar]

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