Skip to main content
SpringerLink
Log in

Speed Control of Vane-Type Air Motor Servo System Using Proportional-Integral-Derivative-Based Fuzzy Neural Network

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

A novel proportional-integral-derivative-based fuzzy neural network (PID-based FNN) controller is proposed in this study to control the speed of a vane-type air motor (VAM) servo system for tracking periodic speed command. First, the structure and operating principles of the VAM servo system are introduced. Then, the dynamics of the VAM servo system is analyzed to derive the second-order state equation of the VAM. Moreover, due to the dynamic characteristics and system parameters of the VAM servo system are highly nonlinear and time-varying, a PID-based FNN controller, which integrates conventional proportional-integral-derivative neural network (PIDNN) control with fuzzy rules, is proposed to achieve precise speed control of VAM servo system under the occurrences of the inherent nonlinearities and external disturbances. The network structure and its on-line learning algorithm using delta adaptation law are described in detail. Meanwhile, the convergence analysis of the speed tracking error is given using the discrete-type Lyapunov function. To enhance the control performance of the proposed intelligent control approach, a 32-bit floating-point digital signal processor (DSP), TMS320F28335, is adopted for the implementation of the proposed control system. Finally, experimental results are illustrated to show the validity and advantages of the proposed PID-based FNN controller for VAM servo system.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Saidur, R., Rahim, N.A., Hasanuzzaman, M.: A review on compressed-air energy use and energy savings. Renew. Sustain. Energy Rev. 14(4), 1135–1153 (2010)

    Article  Google Scholar 

  2. Huang, K.D., Tzeng, S.C.: Development of a hybrid pneumatic-power vehicle. Appl. Energy 80, 47–59 (2005)

    Article  Google Scholar 

  3. Hwang, Y.R., Huang, S.Y.: System identification and integration design of an air/electric motor. Energies 6, 921–933 (2013)

    Article  Google Scholar 

  4. Shen, Y.T., Hwang, Y.R.: Design and implementation of an air-powered motorcycles. Appl. Energy 86, 1105–1110 (2009)

    Article  Google Scholar 

  5. Lu, C.H., Hwang, Y.R., Shen, Y.T.: Backstepping sliding mode tracking control of a vane-type air motor X-Y table motion system. ISA Trans. 50, 278–286 (2011)

    Article  Google Scholar 

  6. Takemura, F., Mizutani, H., Pandian, S. R., Hayakawa, Y., Nagase, Y., Kawamura, S.: Control of a hybrid pneumatic/electric motor. In: Proceedings of the 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 209–214. (2000)

  7. Wang, J., Pu, J., Moore, P.: Accurate position control of servo pneumatic actuator systems: an application to food packaging. Control Eng. Pract. 7, 699–706 (1999)

    Article  Google Scholar 

  8. Dov, D.B., Salcudean, S.E.: A force-controlled pneumatic actuator. IEEE Trans. Robot. Autom. 11, 906–911 (1995)

    Article  Google Scholar 

  9. Taleb, M., Levant, A., Plestan, F.: Pneumatic actuator control: solution based on adaptive twisting and experimentation. Control Eng. Pract. 21, 727–736 (2013)

    Article  Google Scholar 

  10. Plestan, F., Shtessel, Y., Bre’geault, V., Poznyak, A.: Sliding mode control with gain adaptation—application to an electropneumatic actuator. Control Eng. Pract. 21, 679–688 (2013)

    Article  Google Scholar 

  11. Rahmat, M.F., Salim, S.N.S., Sunar, N.H., Faudz, A.A.M., Ismail, Z.H., Huda, K.: Identification and non-linear control strategy for industrial pneumatic actuator. Int. J. Phys. Sci. 7(17), 2565–2579 (2012)

    Article  Google Scholar 

  12. Ahn, K., Yokota, S.: Intelligent switching control of pneumatic actuator using on/off solenoid valves. Mechatronics 15, 683–702 (2005)

    Article  Google Scholar 

  13. Messina, A., Giannoccaro, N.I., Gentile, A.: Experimenting and modelling the dynamics of pneumatic actuators controlled by the pulse width modulation (PWM) technique. Mechatronics 15, 859–881 (2005)

    Article  Google Scholar 

  14. Tokhi, M.O., Al-Miskiry, M., Brisland, M.: Real-time control of air motors using a pneumatic H-bridge. Control Eng. Pract. 9, 449–457 (2001)

    Article  Google Scholar 

  15. Lu, C.H., Hwang, Y.R.: Modeling of an air motor servo system and robust sliding mode controller design. J. Mech. Sci. Technol. 26(4), 1161–1169 (2012)

    Article  MathSciNet  Google Scholar 

  16. Hwang, Y.R., Da Shen, Y., Jen, K.K.: Fuzzy MRAC controller design for vane-type air motor systems. J. Mech. Sci. Technol. 22, 497–505 (2008)

    Article  Google Scholar 

  17. Marumo, R., Tokhi, M.O.: Neural-model reference control of an air motor. In: 7th AFRICON Conference in Africa, vol. 1, pp. 467–472, 17-17 September (2004)

  18. Votrubec, R., Vavrousek, M.: Control system of a rotary pneumatic motor. In: 2014 16th International Conference on Mechatronics—Mechatronika (ME), pp. 588–593, 3–5 December 2014

  19. Crowe, J., Control, P.I.D.: New Identification and Design Methods. Springer, London (2005)

    Google Scholar 

  20. Yamamoto, T., Takao, K., Yamada, T.: Design of a data-driven PID controller. IEEE Trans. Control Syst. Technol. 17(1), 29–39 (2009)

    Article  Google Scholar 

  21. Ren, T.J., Chen, T.C.: Motion control for a two-wheeled vehicle using a self-tuning PID controller. Control Eng. Pract. 16(3), 365–375 (2008)

    Article  MathSciNet  Google Scholar 

  22. Cong, S., Liang, Y.: PID-like neural network nonlinear adaptive control for uncertain multivariable motion control systems. IEEE Trans. Ind. Electron. 56(10), 3872–3879 (2009)

    Article  Google Scholar 

  23. Chen, S.Y., Lin, F.J.: Decentralized PID neural network control for five degree-of-freedom active magnetic bearing. Eng. Appl. Artif. Intell. 26(3), 962–973 (2013)

    Article  Google Scholar 

  24. Su, X., Wu, L., Shi, P., Song, Y.D.: A novel approach to output feedback control of fuzzy stochastic systems. Automatica 50(12), 3268–3275 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  25. Tong, S., Sui, S., Li, Y.: Fuzzy adaptive output feedback control of MIMO nonlinear systems with partial tracking errors constrained. IEEE Trans. Fuzzy Syst. 23(4), 729–742 (2015)

    Article  Google Scholar 

  26. Li, Y., Tong, S., Li, T.: Observer-based adaptive fuzzy tracking control of MIMO stochastic nonlinear systems with unknown control directions and unknown dead zones. IEEE Trans. Fuzzy Syst. 23(4), 1228–1241 (2015)

    Article  Google Scholar 

  27. Su, X., Shi, P., Wu, L., Song, Y.D.: Fault detection filtering for nonlinear switched stochastic systems. IEEE Trans. Autom. Control (2015). doi:10.1109/TAC.2015.2465091

    MathSciNet  Google Scholar 

  28. Li, H., Yin, S., Pan, Y., Lam, H.K.: Model reduction for interval type-2 Takagi-Sugeno fuzzy systems. Automatica 61, 308–314 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  29. Li, H., Pan, Y., Zhou, Q.: Filter design for interval type-2 fuzzy systems with D stability constraints under a unified frame. IEEE Trans. Fuzzy Syst. 23(3), 719–725 (2015)

    Article  Google Scholar 

  30. Wu, L., Yang, X., Lam, H.K.: Dissipativity analysis and synthesis for discrete-time T-S fuzzy stochastic systems with time-varying delay. IEEE Trans. Fuzzy Syst. 22(2), 380–394 (2014)

    Article  Google Scholar 

  31. Lin, F.J., Shieh, H.J., Huang, P.K., Teng, L.T.: Adaptive control with hysteresis estimation and compensation using RFNN for piezo-actuator. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 53(9), 1649–1661 (2006)

    Article  Google Scholar 

  32. Wang, W.Y., Li, I.H., Li, S.C., Tsai, M.S., Su, S.F.: A dynamic hierarchical fuzzy neural network for a general continuous function. Int. J. Fuzzy Syst. 11(2), 130–136 (2009)

    Google Scholar 

  33. Er, M.J., Liu, F., Li, M.B.: Self-constructing fuzzy neural networks with extended Kalman filter. Int. J. Fuzzy Syst. 12(1), 66–72 (2010)

    Google Scholar 

  34. Ko, C.N.: Identification of Chaotic system using fuzzy neural networks with time-varying learning algorithm. Int. J. Fuzzy Syst. 14(4), 540–548 (2012)

    Google Scholar 

  35. Lu, C.H., Tai, C.C., Chen, T.C., Wang, W.C.: Fuzzy neural network speed estimation method for induction motor speed sensorless control. Int. J. Innov. Comput. Inf. Control 11(2), 433–446 (2015)

    Google Scholar 

  36. Li, H.W.: A real-time model of an automotive air propulsion system. MS thesis, National Taiwan Normal University, (2014)

  37. Hung, Y.H., Tung, Y.M., Li, H.W.: A real-time model of an automotive air propulsion system. Appl. Energy 129, 287–298 (2014)

    Article  Google Scholar 

  38. Padian, S.R., Takemura, F., Hayakawa, Kawamura, Y.S.: Control performance of an air motor-can air motors replace electric motors? Conf. Robot Autom. 1, 518–524 (1999)

    Article  Google Scholar 

  39. Lin, F.J., Wai, R.J., Hong, C.M.: Recurrent neural network control for LCC-resonant ultrasonic motor drive. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 47(3), 737–749 (2000)

    Article  Google Scholar 

  40. Lin, F.J., Chen, S.Y., Huang, M.S.: Tracking control of thrust active magnetic bearing system via Hermite polynomial-based recurrent neural network. IET Electr. Power Appl. 4(9), 701–714 (2010)

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge the financial support of the Ministry of Science and Technology in Taiwan, R.O.C. through its Grant MOST 103-2218-E-003 -001.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, National Taiwan Normal University, 106, Taipei, Taiwan

    Syuan-Yi Chen & Sheng-Sian Gong

  2. Department of Industrial Education, National Taiwan Normal University, 106, Taipei, Taiwan

    Yi-Hsuan Hung

Authors
  1. Syuan-Yi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi-Hsuan Hung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sheng-Sian Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Syuan-Yi Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, SY., Hung, YH. & Gong, SS. Speed Control of Vane-Type Air Motor Servo System Using Proportional-Integral-Derivative-Based Fuzzy Neural Network. Int. J. Fuzzy Syst. 18, 1065–1079 (2016). https://doi.org/10.1007/s40815-015-0134-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-015-0134-0

Keywords

Search

Navigation