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Model Estimation and Precise Position Control of an Antagonistic Actuation with Pneumatic Artificial Muscles

공압형 인공근육을 이용한 상극 구동의 모델 추정 및 정밀 위치제어

  • 강봉수 (한남대학교 기계공학과)
  • Received : 2010.11.23
  • Accepted : 2011.03.03
  • Published : 2011.05.01

Abstract

This paper presents a frequency-response test performed on an antagonistic actuation system consisting of two Mckibben pneumatic artificial muscles and a pneumatic circuit with pressure valves. Varying switching frequency to pressure valves from 0.1 Hz to 5 Hz, parameters of a linear model were estimated optimally to predict dynamic characteristics of the antagonistic actuation. A model-base control scheme with estimated parameters was built for the precise trajectory tracking of the antagonistic structure and realized on a reconfigurable embedded control system, CompactRIO. Experimental results showed that the proposed model-based control scheme gave good performance in trajectory tracking comparing with a PD control scheme when square wave and sinusoidal wave were given as references to follow.

Keywords

Pneumatic Artificial Muscle;Antagonistic Actuation;Model-Base Control;Frequency-Response Test

Acknowledgement

Supported by : 한남대학교

References

  1. Bicchi, A. and Tonietti, G., 2004, "Fast and "Soft-Arm" Tactics," IEEE Robotics and Automation Magazine, Vol. 11, pp. 22-33. https://doi.org/10.1109/MRA.2004.1310939
  2. Schulte, H. F., 1961, “The Characteristics of the Mckibben Artificial Muscle,” The Application of External Power in Prosthetics and Orthotics Appendix H, Publication 87, Washington DC: National Academy of Sciences, pp. 94-115.
  3. Daerden, F. and Lefeber, D., 2002, “Pneumatic Artificial Muscles: Actuators for Robotics and Automation,” European Journal of Mechanical and Environment Engineering, Vol. 47, pp.10-21.
  4. Inoue, K., 1988, “Rubbertuators and Applications for Robots,” Proc. of 4th Int. Symp. on Robotics Research, Cambridge USA, pp.57-63.
  5. Chou, C.-P. and Hannaford, B., 1994, “Static and Dynamic Characteristics of Mckibben Pneumatic Artificial Muscles,” Proc. of IEEE Int. Conf. on Robotics and Automation, Vol.1, pp. 281-286. https://doi.org/10.1109/ROBOT.1994.350977
  6. Kothera, C. S., Jangid, M., Sirohi J. and Wereley, N. M., 2006, “Experimental Characterization and Static Modeling of Mckibben Actuators,” Proc. IMECE 2006, Chicago, U.S.A., pp.1-11.
  7. Tondu, B. and Lopez, P., 2000,“Modeling and Control of Mckibben Artificial Muscle Robot Actuators,” IEEE Control Systems Magazine, Vol. 20, No. 2, pp. 15-38. https://doi.org/10.1109/37.833638
  8. Kang, B. S. and Song, S., 2009, “Dynamic Characteristics of an Antagonistic Actuation with Pneumatic Artificial Muscles,” Trans. Of the KSME (A), Vol. 33, No. 10, pp.1081-1086. https://doi.org/10.3795/KSME-A.2009.33.10.1081
  9. Shen, X., 2010, “ Nonlinear Model-Based Control of Pneumatic Artificial Muscle Servo Systems,” Control Engineering Practice, Vol. 18, No.3, pp. 311-317. https://doi.org/10.1016/j.conengprac.2009.11.010
  10. Jutras, D. and Bigras, P., 2006, “Control of an Actuator Made of Two Antagonist Mckibben Muscles via LMI Optimization,” Proc. of IEEE ISIE, Montreal, Canada, pp. 3072-3077. https://doi.org/10.1109/ISIE.2006.296106
  11. Ahn, K. K. and Thanh, T. U. Diep Cong, 2005, “Nonlinear PID Control to Improve the Control Performance of the Pneumatic Artificial Muscle Manipulator Using Neural Network,” Journal of Mechanical Science and Technology, Vol. 19, No. 1, pp. 106-115. https://doi.org/10.1007/BF02916109
  12. Hesselroth, T., Sarkar, K., van der Smaght, P. P. and Schulten, K., 1994, “Neural Network Control of a Pneumatic Robot Arm,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 24, No. 1, pp. 28-38. https://doi.org/10.1109/21.259683
  13. Sanchez, A., Mahout, V. and Tondu, B., 1988, “Nonlinear Parametric Identification of a McKibben Artificial Pneumatic Muscle Using Flatness Property of the System,” Proc. of Int. Conf. on Control Applications, pp. 1-4.
  14. Thongchai, S., Goldfarb, M., Sarkar, N. and Kawamura, K., 2001, “A Frequency Modeling Method of Rubbertuators for Control Application in an IMA Framework,” Proc. of American Control Conference, pp. 1710-1714.
  15. http://www.festo.com
  16. Craig, J. J., 1986, Adaptive Control of Mechanical Manipulator, Ph. D. Dissertation, Stanford University.