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Journal of Mechanical Science and Technology

, Volume 31, Issue 7, pp 3519–3529 | Cite as

Research on PSA-MFAC for a novel bionic elbow joint system actuated by pneumatic artificial muscles

  • Hui Yang
  • Chaoqun Xiang
  • Lina HaoEmail author
  • Liangliang Zhao
  • Bangcan Xue
Article

Abstract

A 3-DOF bionic elbow joint actuated by Pneumatic artificial muscle (PAM) was designed in this paper, and its inverse kinematics model was also established. Then, based on the Model-free adaptive control (MFAC) theory and the effects of control parameters to the control system, a Parameter self-adjust Model-free adaptive control (PSA-MFAC) strategy was proposed, and its adaptability for different control objects was also tested in simulation environment. Combined with the inverse kinematics model, motion control experiments of the bionic elbow joint were conducted in semi-physical platform. Compared with conventional MFAC and PID control algorithm, the experiment results strongly verified the improvement of PSA-MFAC control accuracy. The tracking accuracy of conventional MFAC and PID controller were 9.5 % and 15 %, respectively, in contrast, the PSA-MFAC controller was only 3.8 %. Moreover, complex dynamics modelling of the elbow joint and adjusting process of control parameters were neglected in PSA-MFAC control system.

Keywords

PAM Bionic elbow joint Inverse kinematics model PSA-MFAC 

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References

  1. [1]
    H. A. Baldwin, Realizable models of muscle function, Proceedings of the First Rock Biomechanics Symposium, New York 1969 139–148.Google Scholar
  2. [2]
    D. G. Caldwell, A. Razak and M. J. Goodwin, Braided pneumatic muscle actuators, proceedings of the IFAC conference on intelligent autonomous vehicles, Southampton 1993 507–512.Google Scholar
  3. [3]
    Y.-L. Park, B. Chen, N. O. Pérez-Arancibia, D. Young, L. Stirling, R. J. Wood, E. C. Goldfield and R. Nagpal, Design and control of a bio-inspired soft wearable robotic device for ankle-foot rehabilitation, Bioinspiration & Biomimetics, 9 (2014) 1–17.CrossRefGoogle Scholar
  4. [4]
    Sawicki and Ferris, A pneumatically powered knee-anklefoot orthosis (KAFO) with myoelectric activation and inhibition, Journal of NeuroEngineering and Rehabilitation, 6 (23) (2009) 1–16.Google Scholar
  5. [5]
    E. T. Roche, R. Wohlfarth, J. T. B. Overvelde, N. V. Vasilyev, F. A. Pigula, D. J. Mooney, K. Bertoldi and C. J. Walsh, Actuators: A bioinspired soft actuated material, Advanced Materials, 26 (8) (2014) 1145.CrossRefGoogle Scholar
  6. [6]
    K. Junius, P. Cherelle, B. Brackx, J. Geeroms, T. Schepers, B. Vanderborght and D. Lefeber, On the use of adaptable compliant actuators in prosthetics, rehabilitation and assistive robotics, Robot Motion and Control (2013).Google Scholar
  7. [7]
    J. Wang, Y. Jin and Z. Tang, Mechanism design and realization of joint of pneumatic muscle of manipulator, Machinetool & Hydraulics, 37 (7) (2009) 86–92 (in Chinese).Google Scholar
  8. [8]
    G. Andrikopoulos, G. Nikolakopoulos, I. Arvanitakis and S. Manesis, Switching model predictive control of a pneumatic artificial muscle, International Journal of Control, Automation, and Systems, 11 (6) (2013) 1223–1231.Google Scholar
  9. [9]
    V. T. Jouppila, S. A. Gadsden, G. M. Bone, A. U. Ellman and S. R. Habibi, Sliding mode control of a pneumatic muscle actuator system with a pwm strategy, International Journal of Fluid Power, 15 (1) (2014) 19–31.CrossRefGoogle Scholar
  10. [10]
    R. M. Robinson, C. S. Kothera and N. M. Wereley, Control of a heavy-lift robotic manipulator with pneumatic arti?cial muscles, Actuators, 3 (2014) 41–65.CrossRefGoogle Scholar
  11. [11]
    B.-S. Kang, Compliance characteristic and force control of antagonistic actuation by pneumatic artificial muscles, Meccanica, 49 (2014) 565–574.CrossRefzbMATHGoogle Scholar
  12. [12]
    X. Zhu, G. Tao, B. Yao and J. Cao, Adaptive robust posture control of a parallel manipulator driven by pneumatic muscles, Automatica, 44 (2008) 2248–2257.MathSciNetCrossRefzbMATHGoogle Scholar
  13. [13]
    L.-W. Lee and I.-H. Li, Design and implementation of a robust FNN-based adaptive sliding-mode controller for pneumatic actuator systems, Journal of Mechanical Science and Technology, 30 (1) (2016) 381–396.CrossRefGoogle Scholar
  14. [14]
    L. Liu, J. Li, Y. Liu, J. Leng, J. Zhao and J. Zhao, Electric field induced variation of temperature and entropy in dielectric elastomers, Journal of Mechanical Science and Technology, 29 (1) (2015) 109–114.CrossRefGoogle Scholar
  15. [15]
    B. Tondu, Robust and accurate closed-loop control of mckibben artificial muscle contraction with a linear single integral action, Actuators, 3 (2014) 142–161.CrossRefGoogle Scholar
  16. [16]
    X. Chang and J. H. Lilly, Fuzzy control for pneumatic muscle tracking via evolutionary tuning, Intelligent Automation & Soft Computing, 9 (4) (2013) 227–244.CrossRefGoogle Scholar
  17. [17]
    H. P. H. Anh and K. K. Ahn, Hybrid control of a pneumatic arti?cial muscle(PAM) robot arm using an inverse NARX fuzzy model, Engineering Applications of Articial Intelligence, 24 (2010) 697–716.CrossRefGoogle Scholar
  18. [18]
    Y. Zhu and Z. Hou, Data-driven MFAC for a class of discrete-time nonlinear systems with RBFNN, IEEE Transactions on Neural Networks and Learning Systems, 25 (5) (2014) 1013–1020.CrossRefGoogle Scholar
  19. [19]
    L. dos S. Coelho and A. A. R. Coelho, Model-free adaptive control optimization using a chaotic particle swarm approach, Chaos, Solitons and Fractals, 41 (2009) 2001–2009.CrossRefzbMATHGoogle Scholar
  20. [20]
    L. Yu, W. Tao, A. Wei and W. Yu, Model-free adaptive control for the ball-joint robot driven by PMA group, Robot, 35 (2) (2013) 129–134 (in Chinese).CrossRefGoogle Scholar
  21. [21]
    Y. Gang, L. Baoren and F. Xiaoyun, Parallel manipulator driven by pneumatic muscle actuators, Chinese Journal of Mechanical Engineering, 42 (7) (2006) 39–45 (in Chinese).CrossRefGoogle Scholar
  22. [22]
    L. D. S. Coelho and A. A. R. Coelho, Model-free adaptive control optimization using a chaotic particle swarm approach, Chaos, Solitons & Fractals, 41 (4) (2009) 2001–2009.CrossRefzbMATHGoogle Scholar
  23. [23]
    K. K. Tan and T. H. Lee, S. N. Huang, Adaptive-predictive control of a class of SISO nonlinear systems, Dynamics and Control, 11 (2) (2001) 151–174.MathSciNetCrossRefzbMATHGoogle Scholar
  24. [24]
    B. Zhang and W. D. Zhang, Adaptive predictive functional control of a class of nonlinear systems, ISA Transactions, 45 (2) (2006) 175–183.CrossRefGoogle Scholar
  25. [25]
    G. Feng, A compensating scheme for robot tracking based on neural networks, Robotics and Autonomous Systems, 15 (1995) 100–206.CrossRefGoogle Scholar
  26. [26]
    L. Hao, Y. Chen and Z. Sun, The sliding mode control for different shapes and dimensions of IPMC on resisting its creep characteristics, Smart Materials and Structure, 24 (2015) 045040.CrossRefGoogle Scholar

Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Hui Yang
    • 1
  • Chaoqun Xiang
    • 1
  • Lina Hao
    • 1
    Email author
  • Liangliang Zhao
    • 1
  • Bangcan Xue
    • 1
  1. 1.School of Mechanical Engineering & AutomationNortheastern UniversityShenyangChina

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