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.
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References
H. A. Baldwin, Realizable models of muscle function, Proceedings of the First Rock Biomechanics Symposium, New York 1969 139–148.
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.
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.
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.
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.
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).
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).
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.
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.
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.
B.-S. Kang, Compliance characteristic and force control of antagonistic actuation by pneumatic artificial muscles, Meccanica, 49 (2014) 565–574.
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.
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.
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.
B. Tondu, Robust and accurate closed-loop control of mckibben artificial muscle contraction with a linear single integral action, Actuators, 3 (2014) 142–161.
X. Chang and J. H. Lilly, Fuzzy control for pneumatic muscle tracking via evolutionary tuning, Intelligent Automation & Soft Computing, 9 (4) (2013) 227–244.
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.
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.
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.
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).
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).
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.
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.
B. Zhang and W. D. Zhang, Adaptive predictive functional control of a class of nonlinear systems, ISA Transactions, 45 (2) (2006) 175–183.
G. Feng, A compensating scheme for robot tracking based on neural networks, Robotics and Autonomous Systems, 15 (1995) 100–206.
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.
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Hui Yang was born in Jinzhou, China, in 1987. He received the B.S. degree and M.S. degree in machinery design and manufacture from Liaoning Shihua University, Fushun, China in 2010 and 2013, respectively. He is currently a Ph.D. candidate at the Northeastern University, Shenyang, China. His research interests include modeling and control of PAM and compliance control of the bionic manipulator actuated by artificial muscles. He is a student member of IEEE and International Society of Bionic Engineering.
Lina Hao was born in Zhuanghe, China, in 1968. She received the B.S. degree in machinery design and manufacture from Shenyang Ligong University, Shenyang, China in 1989, M.S. degree in solid mechanics and Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China in 1994 and 2001, respectively. Currently, she is a Professor in Department of Mechanical Engineering and Automation in Northeastern University, China. Her research interests include robot system and intelligent control, intelligent structure and precision motion control system, pattern recognition and condition monitoring. Prof. Hao is selected as a hundredlevel member in "Pacesetter Project" Liaoning province, China, a member of International Society of Bionic Engineering and a member of Chinese Association of Automation System Simulation Discipline and Robot Discipline Committee.
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Yang, H., Xiang, C., Hao, L. et al. Research on PSA-MFAC for a novel bionic elbow joint system actuated by pneumatic artificial muscles. J Mech Sci Technol 31, 3519–3529 (2017). https://doi.org/10.1007/s12206-017-0640-0
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DOI: https://doi.org/10.1007/s12206-017-0640-0