Skip to main content
SpringerLink
Log in

Fault tracking sliding-mode controller design for fuzzy fractional-order system subject to actuator saturation

  • Published:
International Journal of Dynamics and Control Aims and scope Submit manuscript

Abstract

This paper brings forth the fault tracking(estimation) problem for Fractional-Order Takagi-Sugeno Fuzzy(FOTSF) uncertain model subject to time-varying actuator fault and actuator saturation. In order to maintain the stability of considered system, fuzzy fault-tolerant sliding-mode controller is constructed based on fast adaptive fault estimation algorithm. Precisely, stability analysis is performed for the FOTSF model based on state and fault estimations by using the Lyapunov’s stability theorem. More precisely, the sufficient constraints for stability of formulated model are built in proposed theorems. Eventually, two numerical exemplars including one chaotic model of Rossler are issued to support the proposed results and to demonstrate the efficacy of prescribed controller.

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. Yang X, Li C, Song Q, Chen J, Huang J (2018) Global Mittag-Leffler stability and synchronization analysis of fractional-order quaternion-valued neural networks with linear threshold neurons. Neural Netw. https://doi.org/10.1016/j.neunet.2018.04.015

    Article  MATH  Google Scholar 

  2. Liu P, Nie X, Liang J, Cao J (2018) Multiple Mittag-Leffler stability of fractional-order competitive neural networks with Gaussian activation functions. Neural Netw 108:452–465

    Article  Google Scholar 

  3. Ali MS, Narayanan G, Sevgen S, Shekher V, Arik S (2019) Global stability analysis of fractional-order fuzzy BAM neural networks with time delay and impulsive effects. Commun Nonlinear Sci Numer Simulat 78:1–12

    MathSciNet  MATH  Google Scholar 

  4. Shahri ESA, Alfi A, Machado JT (2017) Robust stability and stabilization of uncertain fractional order systems subject to input saturation. J Vib Control. https://doi.org/10.1177/1077546317708927

    Article  MATH  Google Scholar 

  5. Chen J, Li C, Yang X (2018) Asymptotic stability of delayed fractional-order fuzzy neural networks with impulse effects. J Frankl Inst. https://doi.org/10.1016/j.jfranklin.2018.07.039

    Article  MathSciNet  MATH  Google Scholar 

  6. Chen L, He Y, Chai Y, Wu R (2013) New results on stability and stabilization of a class of nonlinear fractional-order systems. Nonlinear Dyn. https://doi.org/10.1007/s11071-013-1091-5

    Article  Google Scholar 

  7. Lan YH, Zhou Y (2013) Non-fragile observer-based robust control for a class of fractional-order nonlinear systems. Syst Control Lett 62:1143–1150

    Article  MathSciNet  Google Scholar 

  8. Benzaouia A, Hajjaji AE (2014) Advanced Takagi-Sugeno fuzzy systems: delay and saturation. Springer-Verlag, New York

    Book  Google Scholar 

  9. Choi HD, Ahn CK, Shi P, Wu L, Lim MT (2017) Dynamic output-feedback dissipative control for T-S fuzzy systems with time-varying input delay and output constraints. IEEE Trans Fuzzy Syst 25(3):511–526

    Article  Google Scholar 

  10. Li H, Wu C, Jing X, Ligang Wu (2017) Fuzzy tracking control for nonlinear networked systems. IEEE Trans Cybern 47(8):2020–2031

    Article  Google Scholar 

  11. Wang Y, Karimi HR, Lam HK, Shen H (2018) An improved result on exponential stabilization of sampled-data fuzzy systems. IEEE Trans Fuzzy Syst 26(6):3875–3883

    Article  Google Scholar 

  12. Liu YJ, Gong M, Tong S, Chen CLP, Li DJ (2018) Adaptive fuzzy output feedback control for a class of nonlinear systems with full state constraints. IEEE Trans Fuzzy Syst 26(5):2607–2617

    Article  Google Scholar 

  13. Chang X-H, Yang C, Xiong J (2019) Quantized fuzzy output feedback H\(_\infty \) control for nonlinear systems with adjustment of dynamic parameters. IEEE Trans Syst Man Cybern Syst 49(10):2005–2015

  14. Cheng J, Park JH, Zhang L, Zhu Y (2018) An asynchronous operation approach to event-triggered control for fuzzy Markovian jump systems with general switching policies. IEEE Trans Fuzzy Syst 26(1):6–18

    Article  Google Scholar 

  15. Shen H, Men Y, Wu ZG, Park JH (2018) Nonfragile H\(_\infty \) control for fuzzy Markovian jump systems under fast sampling singular perturbation. IEEE Trans Syst Man Cybern Syst 48(12):2058–2069

    Article  Google Scholar 

  16. Liu YJ, Li J, Tong S, Chen CLP (2016) Neural network control-based adaptive learning design for nonlinear systems with full-state constraints. IEEE Trans Neural Netw Learn Syst 27(7):1562–1571

    Article  MathSciNet  Google Scholar 

  17. Manivannan R, Samidurai R, Cao J, Alsaed A, Alsaadi FE (2017) Global exponential stability and dissipativity of generalized neural networks with time-varying delay signals. Neural Netw 87:149–159

    Article  Google Scholar 

  18. He W, Chen Y, Yin Z (2016) Adaptive neural network control of an uncertain robot with full-state constraints. IEEE Trans Cybern 46(3):620–629

    Article  Google Scholar 

  19. Chen CLP, Liu YJ, Wen GX (2014) Fuzzy neural network-based adaptive control for a class of uncertain nonlinear stochastic systems. IEEE Trans Cybern 44(5):583–593

    Article  Google Scholar 

  20. Zheng Y, Nian Y, Wang D (2010) Controlling fractional order chaotic systems based on Takagi-Sugeno fuzzy model and adaptive adjustment mechanism. Phys Lett A 375:125–129

    Article  Google Scholar 

  21. Huang X, Wang Z, Li Y, Luc J (2014) Design of fuzzy state-feedback controller for robust stabilization of uncertain fractional-order chaotic systems. J Frankl Inst 351:5480–5493

    Article  MathSciNet  Google Scholar 

  22. Wang B, Xue J, Chen D (2014) Takagi-Sugeno fuzzy control for a wide class of fractional-order chaotic systems with uncertain parameters via linear matrix inequality. J Vib Control 22(10):1–14

    MathSciNet  Google Scholar 

  23. Lin TC, Kuo CH (2011) H\(_\infty \) synchronization of uncertain fractional order chaotic systems: adaptive fuzzy approach. ISA Trans 50:548–556

    Article  Google Scholar 

  24. Liu H, Pan Y, Li S, Chen Y (2017) Adaptive fuzzy backstepping control of fractional-order nonlinear systems. IEEE Trans Syst Man Cybern Syst 47(8):2209–2217

    Article  Google Scholar 

  25. Pan Y, Du P, Xue H, Lam H (2020) Singularity-free fixed-time fuzzy control for robotic systems with user-defined performance. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2020.2999746

  26. Liang H, Liu G, Zhang H, Huang T (2020) Neural-network-based event-triggered adaptive control of nonaffine nonlinear multiagent systems with dynamic uncertainties. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2020.3003950

    Article  Google Scholar 

  27. Chen J, Patton RJ (1999) Robust model-based fault diagnosis for dynamic systems. Kluwer Academic Publishers, Massachusetts

    Book  Google Scholar 

  28. Isermann R (2006) Fault-diagnosis systems: an introduction from fault detection to fault tolerance. Springer, Berlin, Germany

    Book  Google Scholar 

  29. You F, Li H, Wang F, Guan S (2015) Robust fast adaptive fault estimation for systems with time-varying interval delay. J Frankl Inst 352(12):5486–5513

    Article  MathSciNet  Google Scholar 

  30. You F, Li M (May 2017) Fault tolerant control for T-S fuzzy systems with simultaneous actuator and sensor faults, In: 2017 29\(^th\) Chinese Control And Decision Conference (CCDC) Date of Conference 28-30

  31. Liu H, Pan Y, Li S, Chen Y (2019) Fault-tolerant control for Markovian jump systems with general uncertain transition rates against simultaneous actuator and sensor faults. Int J Robust Nonlinear Control 347:169–193

    Google Scholar 

  32. Li H, Wu Y, Chen M (2021) Adaptive fault-tolerant tracking control for discrete-time multiagent systems via reinforcement learning algorithm. IEEE Trans Cybern 51(3):1163–1174

    Article  Google Scholar 

  33. Doye IN, Kirati TML (2015) Fractional-order adaptive fault estimation for a class of nonlinear fractional-order systems, In: American Control Conference (ACC), https://doi.org/10.1109/ACC.2015.7171923

  34. Dhanalakshmi P, Senpagam S, Priya RM (2019) Robust fault estimation controller for fractional-order delayed system using quantized measurement. Int J Dyn Control. https://doi.org/10.1007/s40435-019-00549-2

    Article  Google Scholar 

  35. Wang Y, Shen H, Karimi HR, Duan D (2018) Dissipativity-based fuzzy integral sliding mode control of continuous continuous-time T-S fuzzy systems. IEEE Trans Fuzzy Syst 26(3):1164–1176

    Article  Google Scholar 

  36. Li R, Zhang Q (2018) Robust H\(_\infty \) sliding mode observer design for a class of Takagi-Sugeno fuzzy descriptor systems with time-varying delay. Appl Math Comput 337:158–178

    MathSciNet  MATH  Google Scholar 

  37. Si-Ammour A, Djennoune S, Bettayeb M (2009) A sliding mode control for linear fractional systems with input and state delays. Commun Nonlinear Sci Numer Simul 14(5):2310–2318

    Article  MathSciNet  Google Scholar 

  38. Lin TC, Lee TY, Balas VE (2011) Adaptive fuzzy sliding mode control for synchronization of uncertain fractional order chaotic systems. Chaos Solitons Fractals 44(10):791–801

    Article  Google Scholar 

  39. Xu S, Sun G, Ma Z, Li X (2019) Fractional-order fuzzy sliding mode control for the deployment of tethered satellite system under input saturation. IEEE Trans Aerosp Electron Syst 55(2):747–756

    Article  Google Scholar 

  40. Cao YY, Lin Z (2003) Robust stability analysis and fuzzy-scheduling control for nonlinear systems subject to actuator saturation. IEEE Trans Fuzzy Syst 11(1):57–67

    Article  Google Scholar 

  41. Dang QV, Vermeiren L, Dequidt A, Dambrine M (2017) Robust stabilizing controller design for Takagi-Sugeno fuzzy descriptor systems under state constraints and actuator saturation. Fuzzy Sets Syst 329:77–90

    Article  MathSciNet  Google Scholar 

  42. Nasri M, Saifia D, Chadli M (2019) H\(_\infty \) static output feedback control for electrical power steering subject to actuator saturation via fuzzy Lyapunov functions. Trans Inst Meas Control. https://doi.org/10.1177/0142331218824385

    Article  Google Scholar 

  43. Song S, Song X, Balsera IT (2018) Mixed H\(_\infty \) /passive projective synchronization for nonidentical uncertain fractional-order neural networks based on adaptive sliding mode control. Neural Process Lett 47(2):443–462

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Applied Mathematics, Bharathiar University, Coimbatore, 641046, India

    S. Senpagam & P. Dhanalakshmi

  2. Department of Mathematics, Sri Krishna Adithya college of Arts and Science, Affiliated to Bharathiar University, Coimbatore, 641042, India

    R. Mohanapriya

Authors
  1. S. Senpagam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Dhanalakshmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Mohanapriya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Dhanalakshmi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Senpagam, S., Dhanalakshmi, P. & Mohanapriya, R. Fault tracking sliding-mode controller design for fuzzy fractional-order system subject to actuator saturation. Int. J. Dynam. Control 10, 270–282 (2022). https://doi.org/10.1007/s40435-021-00794-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40435-021-00794-4

Keywords

Search

Navigation