An ANFIS based inverse modeling for pneumatic artificial muscles

Year 2020, Volume: 8 Issue: 3, 50 - 56, 01.10.2020

Cabbar Veysel Baysal

https://doi.org/10.18100/ijamec.797271

Abstract

Pneumatic Artificial Muscles (PAM) are soft actuators with advantages of high force to weight ratio, flexible structure and low cost. On the other hand, their inherent nonlinear characteristics yield difficulties in modeling and control actions, which is an important factor restricting use of PAM. In literature, there are various modeling approaches such as virtual work , empirical and phenomenological models. However, they appear as either much complicated or are approximate ones as a variable stiffness spring for model with nonlinear input-output relationship. In this work, the behaviour of PAM is interpreted as an integrated response to pressure input that results in a simultaneous force and muscle length change. The integrated response behaviour of PAM is not combined effectively in terms of simultaneous resultant force and muscle contraction in many existing models. In order to implement that response, standard identification methods , for instance NNARX, are not suitable for modeling this behaviour. Moreover, an inverse modeling with grey box approach is proposed in order to utilize the model in control applications. Since Neuro-Fuzzy inference systems are universal estimators, the modeling is implemented by an ANFIS structure using the experimental data collected from PAM test bed. According to implementation results, the ANFIS based inverse model has yielded satisfactory performance deducing that it could be a simple and effective solution for PAM modeling and control issue.

Keywords

Soft Actuators, Pneumatic Artificial Muscles, Neuro-Fuzzy Modeling, Inverse Modeling, ANFIS

References

• 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.
• B. Tondu , "Modelling of the McKibben artificial muscle: A review", Journal of Intelligent Material Systems and Structures, vol 23-3, pp. 225–253, 2012.
• 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.
• 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.
• C.P. Chou and B. Hannaford, "Measurement and modeling of McKibben pneumatic artificial muscles", IEEE Trans. Robot.Automation, vol. 12 , pp. 90–102, 1996.
• 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.
• 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.
• K.C. Wickramatunge and T. Leephakpreeda , " Empirical modeling of dynamic behaviors of pneumatic artificial muscle actuators", ISA Transactions, vol. 52 pp. 825-834, 2013.
• 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.
• 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.
• 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.
• 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.
• Festo Fluidic Muscle DMSP/MAS Info 501, www.festo.com/rep/en_corp/assets/pdf/info_501_en.pdf, 2018.
• 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.
• J.S.R. Jang, "ANFIS: Adaptive-Network-Based Fuzzy Inference System", IEEE Trans. Systems, Man, and Cybernetics, vol. 23, pp. 665-684, 1993.
• 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 .