Skip to main content
Log in

Adaptive fuzzy command filtered control for incommensurate fractional-order MIMO nonlinear systems with input saturation

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In this paper, an adaptive fuzzy control approach for incommensurate fractional-order multi-input multi-output (MIMO) systems with unknown nonlinearities and input saturation is presented. First, the nonlinear terms of MIMO systems are identified by introducing the fuzzy logic systems, and an adaptive compensating control term is provided to estimate the approximation errors. Then, the drawback of “explosion of complexity” in the typical backstepping is effectively figured out via an improved command filter, and the influence of filtered error is avoided by constructing the error compensation laws. Meanwhile, the input saturation issue is addressed by utilizing the fractional-order auxiliary equations. Derived from the fractional-order Lyapunov stability theory, it is proved that all signals of the closed-loop system are guaranteed to be bounded. Finally, the availability of the investigated control scheme is verified by simulation examples.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availibility statements

The data are available from the corresponding author on reasonable request.

References

  1. Liu Y, Su H (2022) Necessary and sufficient conditions for containment in fractional-order multiagent systems via sampled data. IEEE Transact Syst Man Cybern: Syst 52(1):238–246

    Google Scholar 

  2. Luo S, Lewis FL, Song Y, Ouakad HM (2021) Accelerated adaptive fuzzy optimal control of three coupled fractional-order chaotic electromechanical transducers. IEEE Transact Fuzzy Syst 29(7):1701–1714

    Google Scholar 

  3. Salman I, Lin Y, Hamayun MT (2018) Fractional order modeling and control of dissimilar redundant actuating system used in large passenger aircraft. Chin J Aeronaut 31(5):1141–1152

    Google Scholar 

  4. Lu S, Wang X (2021) Barrier lyapunov function-based adaptive neural network control for incommensurate fractional-order chaotic permanent magnet synchronous motors with full-state constraints via command filtering. J Vibrat Cont 27(21–22):2574–2585

    MathSciNet  Google Scholar 

  5. Baleanu D, Asad JH, Petras I (2015) Numerical solution of the fractional Euler-Lagrange’s equations of a thin elastica model. Nonlinear Dynam 81(1):97–102

    MathSciNet  MATH  Google Scholar 

  6. Baleanu D, Ghanbari B, Asad JH, Jajarmi A, Pirouz HM (2020) Planar system-masses in an equilateral triangle: numerical study within fractional calculus. Comput Model Eng Sci 124(3):953–968

    Google Scholar 

  7. Hasan EH, Asad JH (2017) Remarks on fractional Hamilton Jacobi formalism with second-order discrete Lagrangian systems. J Adv Phys 6(3):430–433

    Google Scholar 

  8. Yu Z, Zhang Y, Jiang B, Su CY, Fu J, Jin Y, Chai T (2021) Fractional-order adaptive fault-tolerant synchronization tracking control of networked fixed-wing uavs against actuator-sensor faults via intelligent learning mechanism. IEEE Transact Neural Netw Learn Syst 32(12):5539–5553

    MathSciNet  Google Scholar 

  9. Danca MF, Fečkan M, Chen G (2017) Impulsive stabilization of chaos in fractional-order systems. Nonlinear Dyn 89(3):1889–1903

    MathSciNet  Google Scholar 

  10. Odibat Z (2021) A universal predictor-corrector algorithm for numerical simulation of generalized fractional differential equations. Nonlinear Dyn 105(3):2363–2374

    MathSciNet  Google Scholar 

  11. Seblani YE, Shivanian E (2021) New insight into meshless radial point Hermite interpolation through direct and inverse 2-D reaction-diffusion equation. Eng Comput 37(4):3605–3613

    Google Scholar 

  12. Hosseini VR, Yousefi F, Zou WN (2021) The numerical solution of high dimensional variable-order time fractional diffusion equation via the singular boundary method. J Adv Res 32:73–84

    Google Scholar 

  13. Hosseini VR, Zou W (2022) The peridynamic differential operator for solving time-fractional partial differential equations. Nonlinear Dyn 109:1823–1850

    Google Scholar 

  14. Hosseini VR, Zheng H, Zou W (2022) An efficient meshfree computational approach to the analyze of thermoelastic waves of functionally graded materials in a two-dimensional space. Alex Eng J 61(12):10495–10510

    Google Scholar 

  15. Zou W, Tang Y, Hosseini VR et al (2022) The numerical meshless approach for solving the 2d time nonlinear multi-term fractional cable equation in complex geometries. Fractals 30(05):22401701–224017012

    MATH  Google Scholar 

  16. Ahmad S, Ullah A, Akgül A (2021) Investigating the complex behaviour of multi-scroll chaotic system with caputo fractal-fractional operator. Chaos, Solitons Fractals 146:110900

    MathSciNet  MATH  Google Scholar 

  17. Li X, Wen C, Zou Y (2020) Adaptive backstepping control for fractional-order nonlinear systems with external disturbance and uncertain parameters using smooth control. IEEE Transact Syst Man Cybern Syst 51(12):7860–7869

    Google Scholar 

  18. Mohsenipour R, Jegarkandi MF (2019) Fractional order mimo controllers for robust performance of airplane longitudinal motion. Aerosp Sci Technol 91:617–626

    Google Scholar 

  19. Zhao D, Hu Y, Sun W, Zhou X, Xu L, Yan S (2022) A digraph approach to the state-space model realization of mimo non-commensurate fractional order systems. Journal of the Franklin Institute

  20. Almeida AMd, Lenzi MK, Lenzi EK (2020) A survey of fractional order calculus applications of multiple-input, multiple-output (mimo) process control. Fractal Fractional 4(2):22

    Google Scholar 

  21. Ha S, Chen L, Liu H (2021) Adaptive fuzzy variable structure control of fractional-order nonlinear systems with input nonlinearities. Inter J Fuzzy Syst 23(7):2309–2323

    Google Scholar 

  22. Li X, He J, Wen C, Liu XK (2021) Backstepping based adaptive control of a class of uncertain incommensurate fractional-order nonlinear systems with external disturbance. IEEE Transact Ind Electron 69(4):4087–4095

    Google Scholar 

  23. Liang B, Zheng S, Ahn CK, Liu F (2022) Adaptive fuzzy control for fractional-order interconnected systems with unknown control directions. IEEE Transact Fuzzy Syst 30(1):75–87

    Google Scholar 

  24. Alassafi MO, Ha S, Alsaadi FE, Ahmad AM, Cao J (2021) Fuzzy synchronization of fractional-order chaotic systems using finite-time command filter. Inform Sci 579:325–346

    MathSciNet  Google Scholar 

  25. Bataghva M, Hashemi M (2021) Adaptive sliding mode synchronisation for fractional-order non-linear systems in the presence of time-varying actuator faults. IET Cont Theory Appl 12(3):377–383

    MathSciNet  Google Scholar 

  26. Lu S, Wang X, Li Y (2019) Adaptive neural network control for fractional-order PMSM with time delay based on command filtered backstepping. AIP Adv 9(5):055105

    Google Scholar 

  27. Lu S, Wang X (2021) Adaptive neural network output feedback control of incommensurate fractional-order pmsms with input saturation via command filtering and state observer. Neural Comput Appl 33(11):5631–5644

    Google Scholar 

  28. Yang W, Yu W, Lv Y, Zhu L, Hayat T (2021) Adaptive fuzzy tracking control design for a class of uncertain nonstrict-feedback fractional-order nonlinear siso systems. IEEE Transact cybern 51(6):3039–3053

    Google Scholar 

  29. Sui S, Tong S (2021) Ftc design for switched fractional-order nonlinear systems: An application in a permanent magnet synchronous motor system. IEEE Transact Cybern. https://doi.org/10.1109/TCYB.2021.3123377

  30. Wu Y, Liang H, Zhang Y, Ahn CK (2021) Cooperative adaptive dynamic surface control for a class of high-order stochastic nonlinear multiagent systems. IEEE Transact cybern 51(11):5214–5224

    Google Scholar 

  31. Zhao E, Yu J, Liu J, Ma Y (2021) Neuroadaptive dynamic surface control for induction motors stochastic system based on reduced-order observer. ISA transactions. https://doi.org/10.1016/j.isatra.2021.09.006

  32. Farrell JA, Polycarpou M, Sharma M, Dong W (2009) Command filtered backstepping. IEEE Transact Automat Cont 54(6):1391–1395

    MathSciNet  MATH  Google Scholar 

  33. Dong W, Farrell JA, Polycarpou MM, Djapic V, Sharma M (2012) Command filtered adaptive backstepping. IEEE Transact Control Syst Technol 20(3):566–580

    Google Scholar 

  34. Yu J, Chen B, Yu H, Lin C, Zhao L (2018) Neural networks-based command filtering control of nonlinear systems with uncertain disturbance. Inform Sci 426:50–60

    MathSciNet  MATH  Google Scholar 

  35. Homayoun B, Arefi MM, Vafamand N, Yin S (2020) Neuro-adaptive command filter control of stochastic time-delayed nonstrict-feedback systems with unknown input saturation. J Franklin Inst 357(12):7456–7482

    MathSciNet  MATH  Google Scholar 

  36. Wang L, Wang H, Liu PX, Ling S, Liu S (2022) Fuzzy finite-time command filtering output feedback control of nonlinear systems. IEEE Transact Fuzzy Syst 30(1):97–107

    Google Scholar 

  37. Song S, Park JH, Zhang B, Song X, Zhang Z (2021) Adaptive command filtered neuro-fuzzy control design for fractional-order nonlinear systems with unknown control directions and input quantization. IEEE Transact Systems Man Cybern: Syst 51(11):7238–7249

    Google Scholar 

  38. Liu H, Pan Y, Cao J, Wang H, Zhou Y (2020) Adaptive neural network backstepping control of fractional-order nonlinear systems with actuator faults. IEEE Transact Neural Netw Learn Syst 31(12):5166–5177

    MathSciNet  Google Scholar 

  39. Yuan X, Wang Y, Liu J, Sun C (2022) Action mapping: A reinforcement learning method for constrained-input systems. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2021.3138924

  40. Wang C, Cui L, Liang M, Li J, Wang Y (2021) Adaptive neural network control for a class of fractional-order nonstrict-feedback nonlinear systems with full-state constraints and input saturation. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2021.3082984

  41. Wang Y, Liu K, Ji H (2022) Adaptive robust fault-tolerant control scheme for spacecraft proximity operations under external disturbances and input saturation. Nonlinear Dynamics. https://doi.org/10.1007/s11071-021-07182-9

  42. Zhou N, Cheng X, Xia Y, Liu Y (2022) Fully adaptive-gain-based intelligent failure-tolerant control for spacecraft attitude stabilization under actuator saturation. IEEE Transact Cybern 52(1):344–356

    Google Scholar 

  43. Zong G, Sun H, Nguang SK (2021) Decentralized adaptive neuro-output feedback saturated control for ins and its application to auv. IEEE Transact Neural Netw Learn Syst 32(12):5492–5501

    MathSciNet  Google Scholar 

  44. Sheng D, Wei Y, Cheng S, Wang Y (2018) Observer-based adaptive backstepping control for fractional order systems with input saturation. ISA Transact 82:18–29

    Google Scholar 

  45. Cao B, Nie X (2021) Event-triggered adaptive neural networks control for fractional-order nonstrict-feedback nonlinear systems with unmodeled dynamics and input saturation. Neural Netw 142:288–302

    Google Scholar 

  46. Han S (2020) Fractional-order command filtered backstepping sliding mode control with fractional-order nonlinear disturbance observer for nonlinear systems. J Franklin Inst 357(11):6760–6776

    MathSciNet  MATH  Google Scholar 

  47. Podlubny I (1998) Fractional differential equations: an introduction to fractional derivatives, fractional differential equations, to methods of their solution and some of their applications, vol 198. Elsevier, San Diego

    MATH  Google Scholar 

  48. Trigeassou JC, Maamri N, Sabatier J, Oustaloup A (2011) A lyapunov approach to the stability of fractional differential equations. Signal Process 91(3):437–445

    MATH  Google Scholar 

  49. Wang X, Lin H (2011) Design and analysis of a continuous hybrid differentiator. IET Control Theory Appl 5(11):1321–1334

    MathSciNet  Google Scholar 

  50. Mani P, Rajan R, Shanmugam L, Joo YH (2019) Adaptive fractional fuzzy integral sliding mode control for pmsm model. IEEE Transact fuzzy Syst 27(8):1674–1686

    Google Scholar 

Download references

Acknowledgements

The author would thank the support from the National Key Laboratory of Science and Technology on Rotorcraft Aeromechanics (No. 61422202205, 61422202106) and the Aeronautical Science Foundation of China (No. 20175752045).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Senkui Lu.

Ethics declarations

Conflict of interest

The authors confirm that no conflict of interest is existed.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, S., Li, X., Lu, K. et al. Adaptive fuzzy command filtered control for incommensurate fractional-order MIMO nonlinear systems with input saturation. Neural Comput & Applic 35, 8157–8170 (2023). https://doi.org/10.1007/s00521-022-08091-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-08091-7

Keywords

Navigation