Skip to main content
SpringerLink
Log in

Adaptive fuzzy sliding control enhanced by compensation for explicitly unidentified aspects

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

Abstract

Reality shows that 1) the effectiveness of compensators for uncertainty and disturbance (UAD) depends deeply on UAD’s time varying rate (TVR), and 2) controlling a system over a network introduces different constraints and conditions, in which some of these are variable delays in control signal, packet losses, data quantization, safety, and security. This paper presents a new design of fuzzy sliding mode control (FSMC) enhanced by compensation for UAD using a disturbance observer (DO), named DO-FSMC, for a class of nonlinear systems subjected to UAD. First, in order to weaken partly the negative influence of TVR of UAD on the compensation effectiveness, we separate explicitly unidentified aspects into two groups, one related to the model error while the other coming from external disturbances, to distinctly consider. To stamp out the chattering status and reduce calculating cost, we propose an adaptive gain updated directly based on the sliding surface convergence status, to which two new control laws, one for the FSMC and the other for the DO-FSMC, are given via Lyapunov stability analysis. In order to evaluate the DO-FSMC, simulations as well as surveys based on a real semi-active suspension system using a Magnetorheological damper (MRD) with measured datasets are performed. The results obtained from the surveys coincide with the theoretical analysis which show that the competence to stamp out vibration is the advantage of the proposed method compared with the other published methods.

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. S. D. Nguyen, Q. H. Nguyen, and S. B. Choi, “Hybrid clustering based fuzzy structure for vibration control–Part 1: a novel algorithm for building neuro–fuzzy system,” Mechanical Systems and Signal Processing, vol. 50–51, pp. 510–525, 2014. [click]

    Google Scholar 

  2. S. D. Nguyen, Q. H. Nguyen, and S. B. Choi, “Hybrid clustering based fuzzy structure for vibration control–Part 2: an application to semi–active vehicle seat–suspension system,” Mechanical Systems and Signal Processing, vol. 56–57, pp. 288–301, 2014.

    Google Scholar 

  3. J. Wang, A. B. Rad, and P. T. Chan, “Indirect adaptive fuzzy sliding model control–Part I: fuzzy switching,” Fuzzy Sets and Systems, vol. 122, pp. 21–30, 2001. [click]

    Article  MathSciNet  MATH  Google Scholar 

  4. O. Yakut and H. Alli, “Neural based sliding–mode control with moving sliding surface for the seismic isolation of structures,” J. of Vib. and Control, vol. 17, pp. 2103–2116, 2011.

    Article  MATH  Google Scholar 

  5. X. Lingfei and Z. Yue, “Sliding–mode output feedback control for active suspension with nonlinear actuator dynamics,” Journal of Vibration and Control, vol. 21, no. 14, 2015.

    Google Scholar 

  6. H. L. Xing, D. H. Li, J. Li, and C. H. Zhang, “Linear extended state observer based sliding mode disturbance decoupling control for nonlinear multivariable systems with uncertainty,” Inter. Journal of Control, Automation and Systems, vol. 14, no. 4, pp. 967–976, 2016. [click]

    Article  Google Scholar 

  7. R. Mado´nski and P. Herman, “Survey on methods of increasing the efficiency of extended state disturbance observers,” ISA Transactions, vol. 56, pp. 18–27, 2015. [click]

    Google Scholar 

  8. L. Wu, C. Wang, and Q. Zeng, “Observer–based sliding mode control for a class of uncertain nonlinear neutral delay systems,” Journal of the Franklin Institute, vol. 345, pp. 233–253, 2008. [click]

    Article  MathSciNet  MATH  Google Scholar 

  9. A. B. Sharkawy and S. A. Salman, “An adaptive fuzzy sliding mode control scheme for robotic systems,” Intel. Control and Auto., vol. 2, pp. 299–309, 2011.

    Google Scholar 

  10. S. Bououden, M. Chadli, and H. R. Karimi, “Fuzzy sliding mode controller design using Takagi–Sugeno modelled nonlinear systems,” Hindawi Publishing Corporation Mathematical Problems in Engineering, vol. 2013, Article ID 734094, p. 7, 2013.

    MATH  Google Scholar 

  11. D. Ginoya, P. D. Shendge, and S. B. Phadke, “Disturbance observer based sliding mode control of nonlinear mismatched uncertain systems,” Comm. in Nonlinear Science and Num. Simu., vol. 26, pp. 98–107, 2015.

    Article  MathSciNet  Google Scholar 

  12. H. Zhang, Y. Wang, and D. Liu, “Delay–dependent guaranteed cost control for uncertain stochastic fuzzy systems with multiple time delays,” IEEE Transactions on Systems Man and Cybernetics Part B–Cybernetics, vol. 38, no. 1, pp. 126–140, 2008. [click]

    Article  Google Scholar 

  13. H. Zhang, J. Zhang, G. H. Yang, and Y. Luo, “Leader–based optimal coordination control for the consensus problem of multiagent differential games via fuzzy adaptive dynamic programming,” IEEE Tran. on Fuzzy Systems, vol. 23, no. 1, pp. 152–163, 2015. [click]

    Article  Google Scholar 

  14. J. Yang, H. Su, Z. Li, D. Ao, and R. Song, “Adaptive control with a fuzzy tuner for cable–based rehabilitation robot,” Inter. J. of Control, Automation and Systems, vol. 14, no. 3, pp. 865–875, 2016. [click]

    Article  Google Scholar 

  15. H. Zhang, D. Liu, Fuzzy Modeling and Fuzzy Control, Birkhäuser, Boston, 2006.

    MATH  Google Scholar 

  16. S. Yang, C. Li, and T. Huang, “Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control,” Neural Networks, vol. 75, pp. 162–172, 2016. [click]

    Article  Google Scholar 

  17. S. Yang, C. Li, and T. Huang, “Impulsive synchronization for TS fuzzy model of memristor–based chaotic systems with parameter mismatches,” Inter. Journal of Control, Automation and Systems, vol. 14, no. 3, pp. 854–864, 2016. [click]

    Article  Google Scholar 

  18. S. N. Sivanandan, S. Sumathi, and S. N. Deepa, Inductionto Fuzzy Logic Using MATLAB, Springer, Verlag Berlin Heidelberg, 2007.

    Book  Google Scholar 

  19. O. Linda and M. Manic, “General type–2 fuzzy c–means algorithm for uncertain fuzzy clustering,” IEEE Tran. on Fuzzy Systems, vol. 20, no. 5, pp. 883–897, 2012. [click]

    Article  Google Scholar 

  20. R. Hosseini, S. D. Qanadli, S. Barman, M. Mazinani, T. Ellis, and J. Dehmeshki, “An automatic approach for learning and tuning Gaussian interval type–2 fuzzy membership functions applied to lung CAD classification system,” IEEE Transactions on Fuzzy Systems, vol. 20, no. 2, pp. 224–234, 2012. [click]

    Article  Google Scholar 

  21. H. F. Ho, Y. K. Wong, and A. B. Rad, “Adaptive fuzzy sliding mode control with chattering elimination for nonlinear SISO systems,” Simulation Modelling Practice and Theory, vol. 17, pp. 1199–1210, 2009. [click]

    Article  Google Scholar 

  22. V. Nekoukar and A. Erfanian, “Adaptive fuzzy terminal sliding mode control for a class of MIMO uncertain nonlinear systems,” Fuzzy Sets and Systems, vol. 179, pp. 34–49, 2011. [click]

    Article  MathSciNet  MATH  Google Scholar 

  23. O. Castillo and P. Melin, “A review on the design and optimization of interval type–2 fuzzy controllers,” Applied Soft Comp., vol. 12, pp. 1267–1278, 2012. [click]

    Article  Google Scholar 

  24. J. L. Yao and W. K. Shi, “Development of a sliding mode controller for semi–active vehicle suspensions,” J. of Vibration and Control, vol. 19, pp. 1152–1160, 2013.

    Article  Google Scholar 

  25. B. L. Zhang, Q. L. Han, X. M. Zhang, and X. Yu, “Sliding mode control with mixed current and delayed states for offshore steel jacket platforms,” IEEE Trans. on Con. Sys. Tech., vol. 22, pp. 1769–1783, 2013. [click]

    Article  Google Scholar 

  26. J. Song, S. Song, and H. Zhou, “Adaptive nonsingular fast terminal sliding mode guidance law with impact angle constrains,” Inter. J. of Control, Auto. and Systems, vol. 14, no. 1, pp. 99–114, 2016. [click]

    Article  Google Scholar 

  27. H. Alli and O. Yakut, “Application of robust fuzzy slidingmode controller with fuzzy moving sliding surfaces for earthquake–excited structures,” Structural Engineering and Mechanics, vol. 26, pp. 517–544, 2007. [click]

    Article  Google Scholar 

  28. S. Mobayen and S. Javadi, “Disturbance observer and finite–time tracker design of disturbed third–order nonholonomic systems using terminal sliding mode,” Journal of Vibration and Control, vol. 23, no. 2, 2017.

    Google Scholar 

  29. G. Milad and A. Nastaran, “Fuzzy Sliding Mode Control for applying to active vehicle suspensions,” WSEAS Tran. on Systems and Cont., vol. 5, pp. 48–57, 2010.

    Google Scholar 

  30. S. D. Nguyen and Q. H. Nguyen, “Design of active suspension controller for train cars based on sliding mode control, uncertainty observer and neuro–fuzzy system,” Journal of Vibration and Control, vol. 23, no. 8, 2017.

    Google Scholar 

  31. W. H. Chen, “Disturbance observer based control for nonlinear systems,” IEEE/ASME Transactions on Mechatronics, vol. 9, no. 4, pp. 706–710, 2004. [click]

    Article  Google Scholar 

  32. R. Mohammad, H. Mohammad, and R. Mohammad, “An optimal and intelligent control strategy for a class of nonlinear systems: adaptive fuzzy sliding mode,” Journal of Vibration and Control, vol. 22, no. 1, 2016.

    Google Scholar 

  33. P. T. Chan, A. B. Rad, and J. Wang, “Indirect adaptive fuzzy sliding mode control: Part II: parameter projection and supervisory control,” Fuzzy Sets and Systems, vol. 122, pp. 31–43, 2001. [click]

    Article  MathSciNet  MATH  Google Scholar 

  34. M. A. Khanesar, O. Kaynak, S, Yin, and H. Gao, “Adaptive indirect fuzzy sliding mode controller for networked control systems subject to time–varying network–induced time delay,” IEEE Transactions on Fuzzy Systems, vol. 23, no. 1, pp. 205–214, 2015. [click]

    Article  Google Scholar 

  35. W. H. Chen, “Nonlinear disturbance observer–enhanced Dynamic inversion control of missiles,” Journal of Guidance, Control, and Dynamics, vol. 26, no. 1, pp. 161–166, 2003. [click]

    Article  Google Scholar 

  36. H. Li, J. Yu, C. Hilton, and H. Liu, “Adaptive sliding–mode control for nonlinear active suspension vehicle systems using T–S fuzzy approach,” IEEE Trans. on Indus. Electronics, vol. 60, no. 8, pp. 3328–3338, 2013. [click]

    Article  Google Scholar 

  37. S. D. Nguyen and T. I. Seo, “Establishing ANFIS and the use for predicting sliding control of active railway suspension systems subjected to uncertainties and disturbances,” Inter. J. of Machine Learning and Cybernetics, Doi: 10.1007/s13042–016–0614–z, 2016.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division of Computational Mechatronics, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Sy Dzung Nguyen

  2. Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Sy Dzung Nguyen

  3. Department of Mechanical Engineering, Smart Structures and Systems Laboratory, Inha University, Incheon, Korea

    Seung-Bok Choi

  4. Department of Mechanical Engineering, Incheon National University, Incheon, Korea

    Tae-Il Seo

Authors
  1. Sy Dzung Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seung-Bok Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tae-Il Seo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tae-Il Seo.

Additional information

Recommended by Associate Editor Sung Jin Yoo under the direction of Editor Euntai Kim. This research was supported by the Post-doctor Research Program (2016) through the Incheon National University (INU), Incheon, Korea.

Sy Dzung Nguyen received the M.E. degree in Manufacturing Engineering from Ho Chi Minh City University of Technology (HCMUT) - VNU in 2001, Ph.D. degree in Applied Mechanics in 2011 from HCMUT. He was a postdoctoral fellow at Inha University, Korea, 2011–2012, at Incheon National University, Korea, 2015–2016. He is currently a Head of Division of Computational Mechatronics, Institute for Computational Science, Ton Duc Thang University, Vietnam. His research interests include artificial intelligence and its applications to nonlinear adaptive control, system identification and managing structure damage. Dr. Nguyen has been the main author of plenty of ISI papers in these fields.

Seung-Bok Choi received the B.Sc. degree in mechanical engineering from Inha University, Korea, 1979, the M.Sc. and Ph.D. degrees from the Michigan State University, U.S.A in 1986 and 1990, respectively. He is currently a dean of graduate school of Inha University and a distinguished fellow professor of mechanical engineering at Inha University. His research interests are control applications using smart materials such as electro-rheological (ER) fluids, magneto-rheological (MR) fluids, piezoelectric materials and shape memory alloys. He has published more than 450 refereed international journal papers and two books in the area of smart materials and their applications.

Tae-il Seo received the Ph.D. Degree in mechanical engineering, Ecole Centrale de Nantes, France, in 1998. From 1998 to 1999, he was a postdoctoral research fellow in Department of Mechanical Engineering in Inha University, Incheon, Korea. From 1999 to 2001, he was a research fellow in the Department of mechanical corporation Laboratory, Inha University of Korea. From 2001 to 2003, he was a researcher in Department of Precision Mold Lab, KITECH (Korea Institute of Industrial Technology), Korea. Currently, he is a Professor in Department of Mechanical Engineering in Incheon National University, Korea. Dr. Seo’s current research interests include Micro End-Milling, Intelligent Manufacturing System, CAD/CAD systems, etc.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nguyen, S.D., Choi, SB. & Seo, TI. Adaptive fuzzy sliding control enhanced by compensation for explicitly unidentified aspects. Int. J. Control Autom. Syst. 15, 2906–2920 (2017). https://doi.org/10.1007/s12555-016-0569-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-016-0569-6

Keywords

Search

Navigation