Skip to main content
SpringerLink
Log in

Switching model predictive control of a pneumatic artificial muscle

  • Control Applications
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

In this article, a switching Model Predictive Controller (sMPC) scheme for the position control of a Pneumatic Artificial Muscle (PAM) is being presented. The control scheme is based on a constrained linear and PieceWise Affine (PWA) system model approximation that is able to capture the high nonlinearities of the PAM and improve the overall model accuracy, and is composed of: a) a feed-forward term regulating control input at specific reference set-points, and b) a switching Model Predictive Controller handling any deviations from the system’s equilibrium points. Extended simulation studies were utilized in order to investigate and evaluate the efficacy of the suggested controller in the positioning problem of a PAM.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. R. Pierce, “Expansible cover,” U.S. Patent No. 2211478, 1940.

    Google Scholar 

  2. P. Warszawska, “Artificial pneumatic muscles,” U.S. Patent No. 6704918, 1967.

    Google Scholar 

  3. C. P. Chou and B. Hannaford, “Measurement and modeling of McKibben pneumatic artificial muscles,” IEEE Trans. on Robotics and Automation, vol. 12, no. 1, pp. 90–102, 1996.

    Article  Google Scholar 

  4. H. F. Schulte, “The characteristics of the McKibben artificial muscle,” The Application of External Power in Prosthetics and Orthotics, pp. 94–115, 1961.

    Google Scholar 

  5. R. H. Gaylord, “Fluid actuated motor system and stroking device,” U.S. Patent No. 9518851958.

  6. J. Yarlott, “Fluid actuator,” U.S. Patent No. 3 645 173, 1972.

    Google Scholar 

  7. V. Nickel, J. Perry, and A. Garrett, “Development of useful function in the severely paralyzed hand,” Journal of Bone and Joint Surgery, vol. 45-A, no. 5, pp. 933–952, 1963.

    Google Scholar 

  8. G. Andrikopoulos, G. Nikolakopoulos, and S. Manesis, “A Survey on applications of Pneumatic Artificial Muscles,” Mediterranean Conference on Control and Automation (MED), pp. 1439–1446, 2011.

    Chapter  Google Scholar 

  9. T. Hesselroth, K. Sarkar, P. Van der Smagt, and K. Schulten, “Neural network control of a pneumatic robot arm,” IEEE Trans. on System Man Cybernetics, vol. 24, no. 1, pp. 28–38, 1994.

    Article  Google Scholar 

  10. G. A. Medrano-Cerda, C. J. Bowler, and D. G. Caldwell, “Adaptive position control of antagonistic pneumatic muscle actuators,” Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots, vol. 1, pp. 378–383, 1995.

    Article  Google Scholar 

  11. D. W. Repperger, K. R. Johnson, and C. A. Phillips, “A VSC position tracking system involving a large scale pneumatic muscle actuator,” Proc. of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171), vol. 4, pp. 4302–4307, 1998.

    Article  Google Scholar 

  12. D. W. Repperger, C. A. Phillips, and M. Krier, “Controller design involving gain scheduling for a large scale pneumatic muscle actuator,” Proc. of the IEEE International Conference on Control Applications (Cat. No.99CH36328), vol. 1, pp. 285–290, 1999.

    Google Scholar 

  13. K. Balasubramanian and K. S. Rattan, “Fuzzy logic control of a pneumatic muscle system using a linearing control scheme,” Proc. of the 22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003, pp. 432–436, 2003.

    Google Scholar 

  14. 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, no. 3, pp. 310–317, Mar. 2003.

    Article  Google Scholar 

  15. J. H. Lilly, “Adaptive tracking for pneumatic muscle actuators in bicep and tricep configurations,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, vol. 11, no. 3, pp. 333–339, September 2003.

    Article  Google Scholar 

  16. K. K. Ahn and T. D. C. Thanh, “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, 2005.

    Article  Google Scholar 

  17. M. V. Damme, B. Vanderborght, B. Verrelst, R. V. Ham, F. Daerden, and D. Lefeber, “Proxy-based sliding mode control of a planar pneumatic manipulator,” The International Journal of Robotics Research, vol. 28, pp. 266–284, February 2009.

    Article  Google Scholar 

  18. T. Choi and J. Lee, “Control of manipulator using pneumatic muscles for enhanced safety,” IEEE Trans. on Industrial Electronics, vol. 57, no. 8, pp. 2815–2825, August 2010.

    Article  MathSciNet  Google Scholar 

  19. Y. Ariga, H. T. T. Pham, M. Uemura, H. Hirai, and F. Miyazaki, “Novel equilibrium-point control of agonist-antagonist system with pneumatic artificial muscles,” Proc. of IEEE International Conference on Robotics and Automation, Saint Paul, Minnesota, pp. 1470–1475, 14–18 May 2012.

    Google Scholar 

  20. E. Sontag, “Nonlinear regulation: the piecewise linear approach,” IEEE Trans. on Automatic Control, vol. 26, no. 2, pp. 346–358, 1981.

    Article  MathSciNet  MATH  Google Scholar 

  21. T. Vo-Minh, T. Tjahjowidodo, H. Ramon, and H. Van Brussel, “A new approach to modeling hysteresis in a pneumatic artificial muscle using the maxwell-slip model,” IEEE/ASME Trans. on Mechatronics, vol. 16, no. 1, pp. 177–186, February 2011.

    Article  Google Scholar 

  22. J. Richalet, A. Rault, J. L. Testud, and J. Papon, “Model predictive heuristic control: applications to industrial processes,” Automatica, vol. 14, no. 5, pp. 413–428, September 1978.

    Article  Google Scholar 

  23. D. Q. Mayne, J. B. Rawlings, C. V. Rao, and P. O. M. Scokaert, “Constrained model predictive control: Stability and optimality,” Automatica, vol. 36, no. 6, pp. 789–814, June 2000.

    Article  MathSciNet  MATH  Google Scholar 

  24. J. M. Maciejowski, Predictive Control with Constraints, Pearson Education, p. 331, 2001.

    Google Scholar 

  25. J. H. Lee, “Model predictive control: review of the three decades of development,” International Journal of Control, Automation, and Systems, vol. 9, no. 3, pp. 415–424, 2011.

    Article  Google Scholar 

  26. B. Tondu and P. Lopez, “Modeling and control of McKibben artificial muscle robot actuators,” IEEE Control Systems Magazine, vol. 20, no. 2, pp. 15–38, 2000.

    Article  Google Scholar 

  27. G. Andrikopoulos, J. Arvanitakis, G. Nikolakopoulos, and S. Manesis, “A switched system modeling approach for a pneumatic muscle actuator,” Proc. of IEEE International Conference on Industrial Technology, 2012.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electrical and Computer Engineering Department, University of Patras, GR-26500, Rio, Achaia, Greece

    George Andrikopoulos, Ioannis Arvanitakis & Stamatis Manesis

  2. Control Engineering Group, Luleå University of Technology, SE-97187, Luleå, Sweden

    George Nikolakopoulos

Authors
  1. George Andrikopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. George Nikolakopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ioannis Arvanitakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Stamatis Manesis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to George Nikolakopoulos.

Additional information

Recommended by Editorial Board member Youngjin Choi under the direction of Editor Hyouk Ryeol Choi.

George Andrikopoulos was born in Patras, Greece, in 1986. He received the Diploma (BSc) of Electrical and Computer Engineering of the University of Patras, Greece, in 2010. He is currently a Ph.D. candidate at the same Department. His current research interests are mainly focused on modeling and control of Pneumatic Artificial Muscles for generic use in the fields of Industrial Automation, Biomimetic and Medical Robotics. He is a student member of IEEE and a member of CSS, RAS and IES.

George Nikolakopoulos is an Assistant Professor in the Faculty of Automatic Control Systems at the Control Engineering Group at the Division of Systems and Interaction, Luleå University of technology, Sweden. His main research interests encompass fields, such as: Networked Controlled Systems, Mechatronics, Wireless Sensor Networks and Actuators, AUV, UAVs, Robotics, Adaptive Control and System Identification. In the past he have been project manager in several R&D projects funded form EU, ESA, and the Greek National Ministry of Research. In year 2003, George has received the Information Societies Technologies (IST) Prize Award, for the best paper that promotes the scopes of the European IST (currently known as ICT). His published scientific work includes more than 90 published International Journals and Conferences in the fields of my interests. Moreover George has served as IPC member for ICIT’2011, CASE’2010, ETFA’2010, ECC’09, MED’09, MIC’09, and MIC’10 international conferences, and have been Associate Editor and Reviewer of several International Journals and conferences.

Ioannis Arvanitakis was born in Kalamata, Greece, in 1986. He is currently a Ph.D. candidate at the Electrical and Computer Engineering Department of the University of Patras. He received his diploma (BSc) from the same department in 2009. His research interests focus primarily on Unmanned Ground Vehicles (UGV), Motion Planning and Obstacle Avoidance, Simultaneous Localization and Mapping (SLAM), Swarm Robotics.

Stamatis Manesis received his Ph.D. from University of Patras, School of Engineering, Greece, in 1986. He is Professor of Industrial Automation in the Division of Systems & Control of the Electrical & Computer Engineering Dept. in the same university. In 1998–99 he was with the Industrial Control Centre of the Strathclyde University and in 2008 with the ETH Zurich as academic visitor. He has designed various Industrial Automation Systems for Hellenic industries. He has published over 90 conference and journal papers and has written 5 textbooks. Main research interests: Industrial Control, Industrial Automation, Industrial Networks, Expert-Fuzzy Control Systems-Intelligent Controllers and SCADA Systems. His research has been funded by several national projects (PABE, EPE, Karatheodori Program). He has participated in various EU Projects as STRIDE/LIGHT, ESPRIT, EKT.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Andrikopoulos, G., Nikolakopoulos, G., Arvanitakis, I. et al. Switching model predictive control of a pneumatic artificial muscle. Int. J. Control Autom. Syst. 11, 1223–1231 (2013). https://doi.org/10.1007/s12555-012-0176-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-012-0176-0

Keywords

Search

Navigation