Published December 30, 2023 | Version CC BY-NC-ND 4.0
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Fuzzy System Approximation based Adaptive Sliding Mode Control for Nonlinear System

Creators

  • 1. Department of Instrumentation Engineering, Jorhat Engineering College, Jorhat Assam, India.

Contributors

Contact person:

  • 1. Department of Instrumentation Engineering, Jorhat Engineering College, Jorhat Assam, India.
  • 2. Department of Electrical Engineering, Jorhat Engineering College, Jorhat Assam, India.

Description

Abstract: In this paper, an adaptive sliding mode control utilizing a fuzzy system approximation is introduced. The fuzzy system is used to approximate the unknown function of an uncertain nonlinear system. The robustness of the system is ensured by the sliding mode control, while the adaptive fuzzy system improves real-time performance. To approximate unknown nonlinearities, a set of fuzzy rules is formulated whose parameters are adjusted in real-time by an adaptive algorithm. The chattering problem of sliding mode control is satisfactorily resolved, and stable operation is assured.

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Additional details

Identifiers

DOI
10.35940/ijeat.B4338.1213223
ISSN
2249-8958

Dates

Accepted
2023-12-15
Manuscript received on 29 November 2023 | Revised Manuscript received on 05 December 2023 | Manuscript Accepted on 15 December 2023 | Manuscript published on 30 December 2023

References

  • L.X.Wang, A Course in Fuzzy Systems and Control, (Prentice-Hall International, Inc., 1996).
  • L.X. Wang, Stable adaptive fuzzy control of nonlinear systems. IEEE Trans. Fuzzy Syst. 1(2), 146–155 (1993). https://doi.org/10.1109/91.227383
  • Sacheul Jeoung, Jongkil Han, Kyumann Im, Woonchul Ham,Byungkook Yo, Adaptive Fuzzy Sliding Mode Control of Nonlinear System : The Second Control Scheme, Proceedings of the 1996 IEEE IECON. 22nd International Conference on Industrial Electronics, Control, and Instrumentation.
  • Li Xin Wang, J M Mendel, Fuzzy Basis functions, Universal Approximation, and Orthogonal Least Squares Learning, IEEE Transactions on Neural Networks,Vol3,No5 september 1992. https://doi.org/10.1109/72.159070
  • Tairen Sun, Yongping Pan , Adaptive Control for Nonaffine Nonlinear Systems Using Reliable Neural Network Approximation, IEEE Access, Volume 5, 2017 https://doi.org/10.1109/ACCESS.2017.2763628
  • V Nekoukar, A Erfanian , Adaptive fuzzy terminal sliding mode control for a class of MIMO uncertain nonlinear systems,Fuzzy Sets and Systems- 179 (2011) 3449,Elsevier. https://doi.org/10.1016/j.fss.2011.05.009
  • B. Chen, X. Liu, K. Liu, C. Lin, Direct adaptive fuzzy control of nonlinear strict feedback systems, Automatica 45 (2009) 1530 – 1535 https://doi.org/10.1016/j.automatica.2009.02.021
  • Utkin, V.I., Guldner, J., and Shi, J. (2009).Sliding Mode Control in Electro mechanical Systems.,London, UK: Taylor /and Francis Publishers, pp. 115-130.
  • J. Park, I.W. Sandberg, Universal approximation using radial basis function networks, Neural Comput. 3 (2) (1991) 246 - 257. https://doi.org/10.1162/neco.1991.3.2.246
  • Tairen Sun, Yongping Pan , Adaptive Control for Nonaffine Nonlinear Systems Using Reliable Neural Network Approximation, IEEE Access, Volume 5, 2017 https://doi.org/10.1109/ACCESS.2017.2763628
  • Chuan-Kai Lin,Nonsingular Terminal Sliding Mode Control of Robot Manipulators Using Fuzzy Wavelet Networks, IEEE Transactions on Fuzzy Systems (Volume:14 , Issue: 6 ),849 - 859,2006 https://doi.org/10.1109/TFUZZ.2006.879982
  • Seul Jung,Improvement of Tracking Control of a Sliding Mode Controller for Robot Manipulators by a Neural Network,International Journal of Control, Automation and Systems 16(2) (2018) 937-943 https://doi.org/10.1007/s12555-017-0186-z
  • Ali Saghafinia, Hew Wooi Ping, Mohammad Nasir Uddin,Khalaf Salloum Gaied, Adaptive Fuzzy Sliding-Mode Control into Chattering-Free IM Drive, IEEE Transactions on Industry Applications,2014. https://doi.org/10.1109/TIA.2014.2328711
  • Yuzheng Guo and Peng-YungWoo, An Adaptive Fuzzy Sliding Mode Controller for Robotic Manipulators, IEEE Transactions On Systems, Man, And Cybernetics Part A: Systems And Humans, Vol. 33, No. 2, March 2003 https://doi.org/10.1109/TSMCA.2002.805804
  • Furjana, M. T., & Bhanumathi, M. (2020). Fuzzy Metric Dimension of Fuzzy Hypercube Qn and Fuzzy Boolean Graphs. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 3, pp. 3690–3698). Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP. https://doi.org/10.35940/ijeat.c6226.029320
  • Radhamani, V., & Dalin, G. (2019). Significance of Artificial Intelligence and Machine Learning Techniques in Smart Cloud Computing: A Review. In International Journal of Soft Computing and Engineering (Vol. 9, Issue 3, pp. 1–7). Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP. https://doi.org/10.35940/ijsce.c3265.099319
  • Sharma, P. (2023). A Fuzzy Approach to Educational Grading Systems "Fuzzy Logic Based Grade Card." In International Journal of Advanced Engineering and Nano Technology (Vol. 10, Issue 6, pp. 1–8). Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP. https://doi.org/10.35940/ijaent.g9582.0610623
  • Boora, R., & Tomar, Dr. V. P. (2023). Exponential-Trigonometry Intuitionistic Fuzzy Divergence Measure. In International Journal of Basic Sciences and Applied Computing (Vol. 9, Issue 5, p. 1). Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP. https://doi.org/10.35940/ijbsac.d0475.019523
  • David, Dr. D. S. (2020). An Intellectual Individual Performance Abnormality Discovery System i n Civic Surroundings. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 5, pp. 2196–2206). Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP. https://doi.org/10.35940/ijitee.e2133.039520