Cover
Vol. 18 No. 2 (2022)

Published: December 31, 2022

Pages: 101-109

Original Article

A Comprehensive Comparison of Different Control Strategies to Adjust the Length of the Soft Contractor Pneumatic Muscle Actuator

Heba Ali Al-Mosawi 1 Alaa Al-Ibadi Turki Y. Abdalla
1Computer Engineering Department, College of Engineering, University of Basrah, Basrah, Iraq
History
  • Received: June 15, 2022
  • Revised: August 12, 2022
  • Accepted: August 12, 2022
  • Available Online: August 12, 2022
DOI

https://doi.org/10.37917/ijeee.18.2.13

Abstract

According to the growing interest in the soft robotics research field, where various industrial and medical applications have been developed by employing soft robots. Our focus in this paper will be the Pneumatic Muscle Actuator (PMA), which is the heart of the soft robot. Achieving an accurate control method to adjust the actuator length to a predefined set point is a very difficult problem because of the hysteresis and nonlinearity behaviors of the PMA. So the construction and control of a 30 cm soft contractor pneumatic muscle actuator (SCPMA) were done here, and by using different strategies such as the PID controller, Bang-Bang controller, Neural network controller, and Fuzzy controller, to adjust the length of the (SCPMA) between 30 cm and 24 cm by utilizing the amount of air coming from the air compressor. All of these strategies will be theoretically implemented using the MATLAB/Simulink package. Also, the performance of these control systems will be compared with respect to the time-domain characteristics and the root mean square error (RMSE). As a result, the controller performance accuracy and robustness ranged from one controller to another, and we found that the fuzzy logic controller was one of the best strategies used here according to the simplicity of the implementation and the very accurate response obtained from this method.

Keywords

References

  1. D. Rus and M. T. Tolley, “Design, fabrication and control of soft robots,” Nature, vol. 521, no. 7553, pp. 467–475, 2015.
  2. H. Jiang et al., “Design, control, and applications of a soft robotic arm,” arXiv Prepr. arXiv2007.04047, 2020.
  3. A. Al-Ibadi, S. Nefti-Meziani, and S. Davis, “Efficient structure-based models for the McKibben contraction pneumatic muscle actuator: The full description of the behaviour of the contraction PMA,” in Actuators, vol. 6, no. 4, p. 32, 2017.
  4. L. A. T. Al Abeach, S. Nefti-Meziani, and S. Davis, “Design of a variable stiffness soft dexterous gripper,” Soft Robot., vol. 4, no. 3, pp. 274–284, 2017.
  5. A. Al-Ibadi, S. Nefti-Meziani, and S. Davis, “Design, implementation and modelling of the single and multiple extensor pneumatic muscle actuators,” Syst. Sci. Control Eng., vol. 6, no. 1, pp. 80–89, 2018.
  6. A. Al-Ibadi, S. Nefti-Meziani, and S. Davis, “Active soft end effectors for efficient grasping and safe handling,” IEEE Access, vol. 6, pp. 23591–23601, 2018.
  7. A. Al-Ibadi, “The Design and Implementation of a Single-Actuator Soft Robot Arm for Lower Back Pain Reduction,” Iraqi J. Electr. Electron. Eng., no. 3RD, 2020.
  8. H. Al-Fahaam, S. Davis, and S. Nefti-Meziani, “The design and mathematical modelling of novel extensor bending pneumatic artificial muscles (EBPAMs) for soft exoskeletons,” Rob. Auton. Syst., vol. 99, pp. 63–74, 2018.
  9. R. Deimel and O. Brock, “A novel type of compliant and underactuated robotic hand for dexterous grasping,” Int. J. Rob. Res., vol. 35, no. 1–3, pp. 161–185, 2016.
  10. J. M. Bern, Y. Schnider, P. Banzet, N. Kumar, and S. Coros, “Soft robot control with a learned differentiable model,” in 2020 3rd IEEE International Conference on Soft Robotics (RoboSoft), 2020, pp. 417–423.
  11. F. Taher, J. Vidler, and J. Alexander, “A characterization of actuation techniques for generating movement in shape-changing interfaces,” Int. J. Human– Computer Interact., vol. 33, no. 5, pp. 385–398, 2017.
  12. L. A. T. Al Abeach, Pneumatic variable stiffness soft robot end effectors. University of Salford (United Kingdom), 2017.
  13. C. P. Chou and B. Hannaford, “Measurement and modeling of McKibben pneumatic artificial muscles,” IEEE Transactions on Robotics and Automation, vol. 12, no. 1. pp. 90–102, 1996. doi: 10.1109/70.481753.
  14. A. Al-Ibadi, S. Nefti-Meziani, and S. Davis, “Valuable experimental model of contraction pneumatic muscle actuator,” in 2016 21st International Conference on Al-Mosawi, Al-Ibadi & Abdalla | 109 Methods and Models in Automation and Robotics (MMAR), pp. 744–749, 2016.
  15. E. Kelasidi, G. Andrikopoulos, G. Nikolakopoulos, and S. Manesis, “A survey on pneumatic muscle actuators modeling,” in 2011 IEEE International Symposium on Industrial Electronics, pp. 1263–1269, 2011.
  16. A. Ryniecki, J. Wawrzyniak, and A. A. Pilarska, “Basic terms of process control: the on-off control system,” Przem. Spożywczy, vol. 69, no. 11, pp. 26–29, 2015.
  17. J. Paulusová and M. Dúbravská, “Application of design of PID controller for continuous systems,” Humusoft. Cz, vol. 1, pp. 2–7, 2012.
  18. A. K. Ho, “Fundamental of PID Control,” PDHonline Course E, vol. 331, 2014.
  19. F. Barkrot and M. Berggren, “Using machine learning for control systems in transforming environments”, Department of Computer and Information Science, Linköping University, 2020.
  20. S. S. Mokri, H. Husain, W. Martono, A. Shafie, and U. K. M. Bangi, “Real time implementation of NARMA-L2 control of a single link manipulator,” Am. J. Appl. Sci., vol. 5, no. 12, pp. 1642–1649, 2008.
  21. M. Jibril, M. Tadese, and E. Alemayehu, “Tank Liquid Level Control using NARMA-L2 and MPC Controllers,” Res. J., vol. 12, no. 7, 2020.
  22. A. Pukrittayakamee, O. De Jesús, and M. T. Hagan, “Smoothing the control action for NARMA-L2 controllers,” in The 2002 45th Midwest Symposium on Circuits and Systems, 2002. MWSCAS-2002., vol. 3, pp. III–III, 2002.
  23. F. Dernoncourt, “Introduction to fuzzy logic,” Massachusetts Inst. Technol., vol. 21, pp. 50–56, 2013.
  24. I. H. Altas and A. M. Sharaf, “A generalized direct approach for designing fuzzy logic controllers in Matlab/Simulink GUI environment,” Int. J. Inf. Technol. Intell. Comput., vol. 1, no. 4, pp. 1–27, 2007.