A recurrent TSK interval type-2 fuzzy neural networks control with online structure and parameter learning for mobile robot trajectory tracking | Applied Intelligence Skip to main content
Springer Nature Link
Log in

A recurrent TSK interval type-2 fuzzy neural networks control with online structure and parameter learning for mobile robot trajectory tracking

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

This paper focuses on the design of a recurrent Takagi-Sugeno-Kang interval type-2 fuzzy neural network RTSKIT2FNN for mobile robot trajectory tracking problem. The RTSKIT2FNN is incorporating the recurrent frame of internal-feedback loops into interval type-2 fuzzy neural network which uses simple interval type-2 fuzzy sets in the antecedent part and the Takagi-Sugeno-Kang (TSK) type in the consequent part of the fuzzy rule. The antecedent part forms a local internal feedback loop by feeding the membership function of each node in the fuzzification layer to itself. Initially, the rule base in the RTSKIT2FNN is empty, after that, all rules are generated by online structure learning, and all the parameters of the RTSKIT2FNN are updated online using gradient descent algorithm with varied learning rates VLR. Through experimental results, we demonstrate the effectiveness of the proposed RTSKIT2FNN for mobile robot control.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

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

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Matraji I, Al Durra A, Haryono A, Al Wahedi K (2018) Trajectory tracking control of skid-steered mobile robot based on adaptive second order sliding mode control. Control Eng Practice 72:167–176

    Article  Google Scholar 

  2. Saradagi A, Muralidharan V, Krishnan V, Menta S, Mahindrakar AD (2017) Formation control and trajectory tracking of nonholonomic mobile robots. IEEE Transactions on Control Systems Technology

  3. Hao Y, Wang J, Chepinskiy SA, Krasnov AJ, Liu S (2017) Backstepping based trajectory tracking control for a four-wheel mobile robot with differential-drive steering. In: 2017 36th Chinese on control conference (CCC). IEEE, pp 4918–4923

  4. Rubagotti M, Della Vedova ML, Ferrara A (2011) Time-optimal sliding-mode control of a mobile robot in a dynamic environment. IET Control Theory Appl 5(16):1916–1924

    Article  MathSciNet  Google Scholar 

  5. Xin L, Wang Q, She J, Li Y (2016) Robust adaptive tracking control of wheeled mobile robot. Robot Auton Syst 78:36–48

    Article  Google Scholar 

  6. Wai RJ, Liu CM (2009) Design of dynamic petri recurrent fuzzy neural network and its application to path-tracking control of nonholonomic mobile robot. IEEE Trans Indus Electron 56(7):2667–2683

    Article  Google Scholar 

  7. Hagras HA (2004) A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE Trans Fuzzy Syst 12(4):524–539

    Article  Google Scholar 

  8. Castillo O, Neyoy H, Soria J, Melin P, Valdez F (2015) A new approach for dynamic fuzzy logic parameter tuning in ant colony optimization and its application in fuzzy control of a mobile robot. Appl Soft Comput 28:150–159

    Article  Google Scholar 

  9. Huq R, Mann GK, Gosine RG (2006) Behavior-modulation technique in mobile robotics using fuzzy discrete event system. IEEE Trans Robot 22(5):903–916

    Article  Google Scholar 

  10. Hou ZG, Zou AM, Cheng L, Tan M (2009) Adaptive control of an electrically driven nonholonomic mobile robot via backstepping and fuzzy approach. IEEE Trans Control Syst Technol 17(4):803–815

    Article  Google Scholar 

  11. Aissa B, Fatima C, Yassine A (2017) Data fusion strategy for the navigation of a mobile robot in an unknown environment using fuzzy logic control. In: 2017 5th International conference on electrical engineering-boumerdes (ICEE-B). IEEE, pp 1–6

  12. Juang CF, Tsao YW (2008) A self-evolving interval type-2 fuzzy neural network with online structure and parameter learning. IEEE Trans Fuzzy Syst 16(6):1411–1424

    Article  Google Scholar 

  13. Aissa BC, Fatima C (2017) Neural networks trained with levenberg-marquardtiterated extended Kalman filter for mobile robot trajectory tracking. J Eng Sci Technol Rev, 10(4)

    Article  Google Scholar 

  14. Yacine A, Fatima C, Aissa B (2015) Trajectory tracking control of a wheeled mobile robot using an adaline neural network. In: 2015 4th International conference on electrical engineering (ICEE). IEEE, pp 1–5

  15. Li Z, Deng J, Lu R, Xu Y, Bai J, Su CY (2016) Trajectory-tracking control of mobile robot systems incorporating neural-dynamic optimized model predictive approach. IEEE Trans Syst Man Cybern Syst 46(6):740–749

    Article  Google Scholar 

  16. Shojaei K (2016) Neural adaptive output feedback formation control of type (m, 0s) wheeled mobile robots. IET Control Theory Appl 11(4):504–515

    Article  MathSciNet  Google Scholar 

  17. Aissa BC, Fatima C (2015) Adaptive neuro-fuzzy control for trajectory tracking of a wheeled mobile robot. In: 2015 4th International conference on electrical engineering (ICEE). IEEE, pp 1–4

  18. Jang JO (2011) Adaptive neuro-fuzzy network control for a mobile robot. J Intell Robot Sys 62(3–4):567–586

    Article  Google Scholar 

  19. Juang CF, Lin CT (1998) An online self-constructing neural fuzzy inference network and its applications. IEEE Trans Fuzzy Syst 6(1):12–32

    Article  Google Scholar 

  20. Lee CH, Chiu MH (2009) Recurrent neuro fuzzy control design for tracking of mobile robots via hybrid algorithm. Expert Syst Appl 36(5):8993–8999

    Article  Google Scholar 

  21. Wai RJ, Lin YW (2013) Adaptive moving-target tracking control of a vision-based mobile robot via a dynamic petri recurrent fuzzy neural network. IEEE Trans Fuzzy Syst 21(4):688–701

    Article  Google Scholar 

  22. Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning-I. Inf Sci 8(3):199–249

    Article  MathSciNet  Google Scholar 

  23. Mendel JM, John RB (2002) Type-2 fuzzy sets made simple. IEEE Trans Fuzzy Syst 10(2):117–127

    Google Scholar 

  24. Abiyev RH, Kaynak O (2010) Type 2 fuzzy neural structure for identification and control of time-varying plants. IEEE Trans Ind Electron 57(12):4147–4159

    Article  Google Scholar 

  25. Lu X, Zhao Y, Liu M (2018) Self-learning interval type-2 fuzzy neural network controllers for trajectory control of a delta parallel robot. Neurocomputing 283:107–119

    Article  Google Scholar 

  26. Kayacan E, Maslim R (2017) Type-2 fuzzy logic trajectory tracking control of quadrotor vtol aircraft with elliptic membership functions. IEEE/ASME Trans Mechatron 22(1):339–348

    Article  Google Scholar 

  27. Lin FJ, Chou PH, et al. (2009) Adaptive control of two-axis motion control system using interval type-2 fuzzy neural network. IEEE Trans Ind Electron 56(1):178–193

    Article  Google Scholar 

  28. Castro JR, Castillo O, Melin P, Rodríguez Díaz A (2009) A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks. Inform Sci 179(13):2175–2193

    Article  Google Scholar 

  29. Lin YY, Chang JY, Lin CT (2014) A tsk-type-based self-evolving compensatory interval type-2 fuzzy neural network (tscit2fnn) and its applications. IEEE Trans Ind Electron 61(1):447–459

    Article  Google Scholar 

  30. Juang CF, Huang RB, Lin YY (2009) A recurrent self-evolving interval type-2 fuzzy neural network for dynamic system processing. IEEE Trans Fuzzy Syst 17(5):1092–1105

    Article  Google Scholar 

  31. Lin CM, Le TL, Huynh TT (2018) Self-evolving function-link interval type-2 fuzzy neural network for nonlinear system identification and control. Neurocomputing 275:2239–2250

    Article  Google Scholar 

  32. Lin CM, Le TL (2017) Pso-self-organizing interval type-2 fuzzy neural network for antilock braking systems. Int J Fuzzy Syst 19(5):1362–1374

    Article  MathSciNet  Google Scholar 

  33. Lin YY, Liao SH, Chang JY, Lin CT et al (2014) Simplified interval type-2 fuzzy neural networks. IEEE Trans Neural Netw Learning Syst 25(5):959–969

    Article  Google Scholar 

  34. Mon YJ, Lin CM, Leng CH (2008) Recurrent fuzzy neural network control for mimo nonlinear systems. Intell Autom Soft Comput 14(4):395–415

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Telecommunications Signals and Systems Laboratory (TSS) for the support given to this research project

Author information

Authors and Affiliations

  1. Department of Electronics, University Amar Telidji, BP 37G, Laghouat, Algeria

    Aissa Bencherif & Fatima Chouireb

Authors
  1. Aissa Bencherif
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Fatima Chouireb
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Aissa Bencherif.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bencherif, A., Chouireb, F. A recurrent TSK interval type-2 fuzzy neural networks control with online structure and parameter learning for mobile robot trajectory tracking. Appl Intell 49, 3881–3893 (2019). https://doi.org/10.1007/s10489-019-01439-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-019-01439-y

Keywords