Advertisement

Position Control of Pneumatic Actuator Using Cascade Fuzzy Self-adaptive PID

  • Mohd Iskandar Putra Azahar
  • Addie IrawanEmail author
  • Raja Mohd Taufika
  • Mohd Helmi Suid
Conference paper
  • 25 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 632)

Abstract

Pneumatic systems are widely used in the industrial automation with its advantages in high power ratio, low cost and cleanliness fluid medium. However, the complex nonlinearities of pneumatics system make this system having difficulty to perform precise motion control especially in providing precise steady state tracking error on rod piston and stable pressure control. To overcome this issue, a cascade control technique named Fuzzy Self-Adaptive PID (CFSAPID) control is proposed. The adaptive tuning by Fuzzy Logic Controller (FLC) is designed as tuner for PID controller. The proposed CFSAPID is simulated and verified on single-piston double acting valve pneumatic system model plant, and compared with single FSAPID controller. Five parameters are focused for analysis including piston rise time, piston settling time, piston velocity, pressure on piston chambers and force friction. The capability of proposed CFSAPID has been successfully verified by simulation studies.

Keywords

Pneumatic actuator Fuzzy logic PID control Motion control 

Notes

Acknowledgements

This work is supported by Universiti Malaysia Pahang (UMP) Research Grant (RDU180398).

References

  1. 1.
    Lai WK, Rahmat M, Wahab APIDN (2012) Modeling and controller design of pneumatic actuator system with control valve. Int J Smart Sens Intell Syst 5(3):624–644Google Scholar
  2. 2.
    Saleem A, Taha B, Tutunji T, Al-Qaisia A (2015) Identification and cascade control of servo-pneumatic system using Particle Swarm Optimization. Simul Model Pract Theory 52:164–179CrossRefGoogle Scholar
  3. 3.
    Haraguchi D, Kanno T, Tadano K, Kawashima K (2015) A Pneumatically driven surgical manipulator with a flexible distal joint capable of force sensing. IEEE Trans Mechatrons 20(6):1–12CrossRefGoogle Scholar
  4. 4.
    Xie S, Mei J, Liu H, Wang Y (2018) Hysteresis modeling and trajectory tracking control of the pneumatic muscle actuator using modified Prandtl-Ishlinskii model. Mech Mach Theory 120:213–224CrossRefGoogle Scholar
  5. 5.
    Rahmat MF, Najib S, Salim S, Sunar NH, Ahmad ‘Athif MF, Zool Hilmi I, Huda K (2012) Identification and non-linear control strategy for industrial pneumatic actuator. Int J Phys Sci 7(17):2565–2579Google Scholar
  6. 6.
    Syed Salim SN et al (2014) Position control of pneumatic actuator using self-regulation nonlinear PID. Math Probl Eng 2014:1–12Google Scholar
  7. 7.
    Salim SNS, Ismail ZH, Rahmat MF, Faudzi AAM, Sunar NH, Samsudin SI (2013) Tracking performance and disturbance rejection of pneumatic actuator system. In: 9th Asian control conference, pp 1–6Google Scholar
  8. 8.
    Ren H, Fan J, Kaynak O (2019) Optimal design of a fractional-order proportional-integer-differential controller for a pneumatic position servo system. IEEE Trans Ind Electron 66(8):6220–6229CrossRefGoogle Scholar
  9. 9.
    Li L, Xie J, Huang J (2013) Fuzzy adaptive PID control of large erecting system. J Theor Appl Inf Technol 47(1):412–418Google Scholar
  10. 10.
    Jiangtao F, Qinhe G, Wenliang G (2017) Mathematical modeling and fuzzy adaptive PID control of erection mechanism. Telecommun, Comput, Electron Control 15(1):254–263Google Scholar
  11. 11.
    Najjari B, Barakati M, Mohammadi A, Fotuhi MJ, Bostanian M (2014) Position control of an electro-pneumatic system based on PWM technique and FLC. ISA Trans 53(2):647–657CrossRefGoogle Scholar
  12. 12.
    Soares dos Santos MP, Ferreira JAF (2014) Novel intelligent real-time position tracking system using FPGA and fuzzy logic. ISA Trans 53(2):402–414CrossRefGoogle Scholar
  13. 13.
    Abu mallouh M (2008) Force velocity control with neural network compensation for contour tracking with pneumatic actuation. Ph.D. Thesis, Queen’s UniversityGoogle Scholar
  14. 14.
    Dehghan B, Surgenor BW (2013) Comparison of fuzzy and neural network adaptive methods for the position control of a pneumatic system. In: 26th IEEE Canadian conference on electrical and computer engineering, pp 1–4Google Scholar
  15. 15.
    Soon CC, Ghazali R, Jaafar HI, Hussien SYS (2017) Sliding mode controller design with optimized PID sliding surface using particle swarm algorithm. Procedia Comput Sci 105:235–239CrossRefGoogle Scholar
  16. 16.
    Yang H, Sun J, Xia Y, Zhao L (2018) Position control for magnetic rodless cylinders with strong static friction. IEEE Trans Ind Electron 65(7):5806–5815CrossRefGoogle Scholar
  17. 17.
    Fan C, Hong GS, Zhao J, Zhang L, Zhao J, Sun L (2019) The integral sliding mode control of a pneumatic force servo for the polishing process. Precis Eng 55:154–170CrossRefGoogle Scholar
  18. 18.
    Junyi C, Binggang C (2011) Fractional-order control of pneumatic position servosystems. Math Probl Eng 2011:1–14CrossRefGoogle Scholar
  19. 19.
    Mu S, Goto S, Shibata S, Yamamoto T (2019) Intelligent position control for pneumatic servo system based on predictive fuzzy control. Comput Electr Eng 75:112–122CrossRefGoogle Scholar
  20. 20.
    Ramezani S, Baghestan K (2018) Observer-based nonlinear precise control of pneumatic servo systems. Proc Inst Mech Eng, Part E: J Process Mech Eng, 0954408918756906Google Scholar
  21. 21.
    Hildebrandt A, Neumann R, Sawodny O (2010) Optimal system design of SISO-servopneumatic positioning drives. IEEE Trans Control Syst Technol 18(1):35–44CrossRefGoogle Scholar
  22. 22.
    Karpenko M, Sepehri N (2004) Design and experimental evaluation of a nonlinear position controller for a pneumatic actuator with friction. In: Proceedings of the 2004 American control conference, pp 5078–5083Google Scholar
  23. 23.
    Zhao L, Xia Y, Yang Y, Liu Z (2017) Multicontroller positioning strategy for a pneumatic servo system via pressure feedback. IEEE Trans Ind Electron 64(6):4800–4809CrossRefGoogle Scholar
  24. 24.
    Faris Hikmat O, Mohd Faudzi AA, Omer Elnimair M, Osman K (2014) PI adaptive neuro-fuzzy and receding horizon position control for intelligent pneumatic actuator. J Teknologi 67(3):17–24Google Scholar
  25. 25.
    Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Mach Stud 7(1):1–13CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Mohd Iskandar Putra Azahar
    • 1
  • Addie Irawan
    • 1
    Email author
  • Raja Mohd Taufika
    • 1
  • Mohd Helmi Suid
    • 1
  1. 1.Robotics and Unmanned Systems Research Group, Faculty of Electrical and Electronics EngineeringUniversiti Malaysia PahangPekanMalaysia

Personalised recommendations