An ANFIS based inverse modeling for pneumatic artificial muscles
Abstract
Author Affiliations
- Cabbar Veysel BAYSAL — Cukurova Universitesifingerprint0000-0003-1490-8725
References (16)
- 1
F. Daerden and D. Lefeber, "Pneumatic artificial muscles: actuators for robotics and automation", European Journal of Mechanical and Environmental Engineering, vol. 47, pp. 10-21,2002.
- 2
B. Tondu , "Modelling of the McKibben artificial muscle: A review", Journal of Intelligent Material Systems and Structures, vol 23-3, pp. 225–253, 2012.
- 3
M. Martens and I. Boblan, "Modeling the Static Force of a Festo Pneumatic Muscle Actuator: A New Approach and a Comparison to Existing Models", Actuators, vol. 6, pp. 2-11, 2017.
- 4
E. Kelasidi, G. Andrikopoulos, G. Nikolakopoulos and S. Manesis,"A Survey on Pneumatic Muscle Actuators Modeling" , Journal of Energy and Power Engineering, vol. 6, pp. 1442-1452, 2012.
- 5
C.P. Chou and B. Hannaford, "Measurement and modeling of McKibben pneumatic artificial muscles", IEEE Trans. Robot.Automation, vol. 12 , pp. 90–102, 1996.
- 6
D.B. Reynolds, D.W. Repperger, C.A. Phillips and G. Bandry,"Modeling the dynamic characteristics of pneumatic muscle", Annals of Biomedical Engineering, vol. 31, pp. 310–317, 2003.
- 7
D. Zhang , X. Zhao, and J. Han, "Active modeling for pneumatic artificial muscle", in Proc. IEEE 14th Int. Workshop Adv. Motion Control, pp. 44–50, 2016.
- 8
K.C. Wickramatunge and T. Leephakpreeda , " Empirical modeling of dynamic behaviors of pneumatic artificial muscle actuators", ISA Transactions, vol. 52 pp. 825-834, 2013.
- 9
T. Ishikawa, Y. Nishiyama and K. Kogiso, "Characteristic Extraction for Model Parameters of McKibben Pneumatic Artificial Muscles", SICE Journal of Control, Measurement, and System Integration, vol. 11, pp. 357-364, 2018.
- 10
K.K. Ahn and H.P.H. Anh, "Comparative study of modeling and identification of the pneumatic artificial muscle (PAM) manipulator using recurrent neural networks", Journal of Mechanical Science and Technology vol. 22 ,pp. 1287-1298, 2008.
- 11
C. Song, S. Xie, Z. Zhou and Y. Hu, "Modeling of pneumatic artificial muscle using a hybrid artificial neural network approach", Mechatronics, vol. 31, pp. 124-131, 2015.
- 12
M. Chavoshian and M. Taghizadeh, "Recurrent neuro-fuzzy model of pneumatic artificial muscle position". Journal of Mechanical Science and Technology, vol. 34, pp. 499–508, 2020.
- 13
Festo Fluidic Muscle DMSP/MAS Info 501, www.festo.com/rep/en_corp/assets/pdf/info_501_en.pdf, 2018.
- 14
N. Siddique and H.Adeli, Computational Intelligence: Synergies of FuzzyLogic, Neural Networks and Evolutionary Computing , ISBN: 978-1-118-33784-4 ,John Wiley & Sons, Ltd. 2013.
- 15
J.S.R. Jang, "ANFIS: Adaptive-Network-Based Fuzzy Inference System", IEEE Trans. Systems, Man, and Cybernetics, vol. 23, pp. 665-684, 1993.
- 16
M. A. Denai, F. Palis and A. Zeghbib, "ANFIS based modelling and control of non-linear systems : a tutorial," IEEE International Conference on Systems, Man and Cybernetics pp. 3433-3438, 2004 .