Skip to main content

Compound Fractional Integral Terminal Sliding Mode Control and Fractional PD Control of a MEMS Gyroscope

  • Chapter
  • First Online:
New Trends in Robot Control

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 270))

Abstract

This paper proposes a compound fractional order integral terminal sliding mode control (FOITSMC) and fractional order proportional-derivative control (FOPD-FOITSMC) for the control of a MEMS gyroscope. In order to improve the robustness of the conventional integral terminal sliding mode control (ITSMC), a fractional integral terminal sliding mode surface is applied. The chattering problem in FOITSMC, which is usually generated by the excitation of fast un-modelled dynamic is the main drawback. A fractional order proportional-derivative controller (FOPD) is employed in order to eliminate chattering phenomenon. The stability of the FOPD-FOITSMC is proved by Lyapunov theory. The performance of the proposed control method is compared with FOITSMC. Numerical simulations clearly verified the effectiveness of the proposed control approach.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Rahmani, M.: MEMS gyroscope control using a novel compound robust control. ISA Trans. 72, 37–43 (2018)

    Article  Google Scholar 

  2. Rabah, K., Ladaci, S.: Fractional adaptive sliding mode control laws for fractional order chaotic systems synchronization. In: 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), pp. 293–302. Tunisia (2016)

    Google Scholar 

  3. Tianyi, Z., Xuemei, R., Yao, Z.: A fractional order sliding mode controller design for spacecraft attitude control system. In: 34th Chinese Control Conference (CCC), pp. 3379–3382 (2015)

    Google Scholar 

  4. Tian, J., Chen, N., Yang, J., Wang, L.: Fractional order sliding model control of active four-wheel steering vehicles. In: International Conference on Fractional Differentiation and Its Applications (ICFDA), pp. 1–5 (2014)

    Google Scholar 

  5. Kuang, Z., Shao, X., Li, X., Sun, G.: High-precision analysis of discrete-time fractional-order sliding mode control. In: Chinese Control and Decision Conference (CCDC), pp. 3083–3087 (2018)

    Google Scholar 

  6. Lin, J.S., Yan, J.J., Yang, Y.S., Liao, T.L.: Based on sliding mode control to synchronize of switched fractional Lorenz systems. International Symposium on Computer Communication Control and Automation (3CA), vol. 2, pp. 278–281 (2010)

    Google Scholar 

  7. Rahmani, M., Ghanbari, A., Ettefagh, M.M.: Hybrid neural network fraction integral terminal sliding mode control of an Inchworm robot manipulator. Mech. Syst. Signal Process. 80, 117–136 (2016)

    Article  Google Scholar 

  8. Rahmani, M., Ghanbari, A., Ettefagh, M.M.: A novel adaptive neural network integral sliding-mode control of a biped robot using bat algorithm. J. Vib. Control 24(10), 2045–2060 (2018)

    Article  MathSciNet  Google Scholar 

  9. Wu, H., Wang, L., Niu, P., Wang, Y.: Global projective synchronization in finite time of nonidentical fractional-order neural networks based on sliding mode control strategy. Neurocomputing 235, 264–273 (2017)

    Article  Google Scholar 

  10. Hu, T., Zhang, X., Zhong, S.: Global asymptotic synchronization of nonidentical fractional-order neural networks. Neurocomputing (2018)

    Google Scholar 

  11. Ding, Z., Shen, Y.: Projective synchronization of nonidentical fractional-order neural networks based on sliding mode controller. Neural Netw. 76, 97–105 (2016)

    Article  Google Scholar 

  12. Efe, M.O.: Fractional fuzzy adaptive sliding-mode control of a 2-DOF direct-drive robot arm. IEEE Trans. Syst. Man Cybern. Part B 38(6), 1561–1570 (2008)

    Google Scholar 

  13. Liu, N., Fei, J.: Adaptive fractional sliding mode control of active power filter based on dual RBF neural networks. IEEE Access 5, 27590–27598 (2017)

    Article  Google Scholar 

  14. Rahmani, M., Rahman, M.H.: Novel robust control of a 7-DOF exoskeleton robot. PloS one 13(9), e0203440 (2018)

    Article  Google Scholar 

  15. Rahmani, M., Ghanbari, A., Ettefagh, M.M.: Robust adaptive control of a bio-inspired robot manipulator using bat algorithm. Expert. Syst. Appl. 56, 164–176 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mehran Rahmani .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Rahmani, M., Rahman, M.H., Ghommam, J. (2020). Compound Fractional Integral Terminal Sliding Mode Control and Fractional PD Control of a MEMS Gyroscope. In: Ghommam, J., Derbel, N., Zhu, Q. (eds) New Trends in Robot Control. Studies in Systems, Decision and Control, vol 270. Springer, Singapore. https://doi.org/10.1007/978-981-15-1819-5_18

Download citation

Publish with us

Policies and ethics