A robust control of a class of induction motors using rough type-2 fuzzy neural networks

  • Mohammad Hosein Sabzalian
  • Ardashir Mohammadzadeh
  • Shuyi Lin
  • Weidong ZhangEmail author
Methodologies and Application


In this paper, a new adaptive control method is presented for a class of induction motors. The dynamics of the system are assumed to be unknown and also are perturbed by some disturbances such as variation of load torque and rotor resistance. A type-2 fuzzy system based on rough neural network (T2FRNN) is proposed to estimate uncertainties. The parameters of T2FRNN are adjusted based on the adaptation laws which are obtained from Lyaponuv stability analysis. The effects of the uncertainties and the approximation errors are compensated by the proposed control method. Simulation results verify the good performance of the proposed control method. Also a numerical comparison is provided to show the effectiveness of the proposed fuzzy system.


Induction motor Rough neural network Type-2 fuzzy systems Robust stability analysis Faulty conditions 



This paper is partly supported by the National Science Foundation of China (61473183, U1509211, 61627810), and National Key R&D Program of China (2017YFE0128500).

Compliance with ethical standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. Ali JA, Hannan M, Mohamed A, Abdolrasol MG (2016) Fuzzy logic speed controller optimization approach for induction motor drive using backtracking search algorithm. Measurement 78:49–62CrossRefGoogle Scholar
  2. Amin F, Fahmi A, Abdullah S (2019) Dealer using a new trapezoidal cubic hesitant fuzzy TOPSIS method and application to group decision-making program. Soft Comput 23(14):5353–5366CrossRefGoogle Scholar
  3. Ammar A, Bourek A, Benakcha A (2017) Nonlinear svm-dtc for induction motor drive using input–output feedback linearization and high order sliding mode control. ISA Trans 67:428–442CrossRefGoogle Scholar
  4. Devi K, Gautam S, Nagaria D (2014) Speed control of 3-phase induction motor using self-tuning fuzzy PID controller and conventional PID controller. Int J Inf Comput Technol 4(12):1185–1193Google Scholar
  5. Dong C, Brandstetter P, Vo HH, Tran TC, Vo DH (2016) Adaptive sliding mode controller for induction motor. In: International conference on advanced engineering theory and applications. Springer, New York, pp 543–553Google Scholar
  6. Erbatur K, Çallı B (2009) Fuzzy boundary layer tuning for sliding mode systems as applied to the control of a direct drive robot. Soft Comput 13(11):1099CrossRefGoogle Scholar
  7. Fahmi A, Abdullah S, Amin F, Khan MSA (2019) Trapezoidal cubic fuzzy number Einstein hybrid weighted averaging operators and its application to decision making. Soft Comput 23(14):5753–5783CrossRefGoogle Scholar
  8. Fekih A (2008) Effective fault tolerant control design for nonlinear systems: application to a class of motor control system. IET Control Theory Appl 2(9):762–772MathSciNetCrossRefGoogle Scholar
  9. Hagras HA (2004) A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE Trans Fuzzy Syst 12(4):524–539CrossRefGoogle Scholar
  10. Hamidzadeh J, Zabihimayvan M, Sadeghi R (2018) Detection of web site visitors based on fuzzy rough sets. Soft Comput 22(7):2175–2188CrossRefGoogle Scholar
  11. Hosein SM, Ardashir M (2019) A new robust control for induction motors. IETE J Res. CrossRefGoogle Scholar
  12. Huang S, Chen M (2016) Constructing optimized interval type-2 TSK neuro-fuzzy systems with noise reduction property by quantum inspired BFA. Neurocomputing 173:1839–1850CrossRefGoogle Scholar
  13. Jain S, Thakur A (2017) Closed loop speed control of induction motor fed by a high performance z-source inverter. Imp J Interdiscip Res 3(2):47–51Google Scholar
  14. Kusagur A, Fakirappa Kodad S, Ram S (2012) Modelling & simulation of an anfis controller for an ac drive. World J Model Simul 8(1):36–49Google Scholar
  15. Li J, Ren H-P, Zhong Y-R (2015) Robust speed control of induction motor drives using first-order auto-disturbance rejection controllers. IEEE Trans Ind Appl 51(1):712–720CrossRefGoogle Scholar
  16. Lin C-M, Le T-L (2017) Pso-self-organizing interval type-2 fuzzy neural network for antilock braking systems. Int J Fuzzy Syst 19(5):1362–1374MathSciNetCrossRefGoogle Scholar
  17. Lin Y-Y, Chang J-Y, Lin C-T (2014) A tsk-type-based self-evolving compensatory interval type-2 fuzzy neural network (TSCIT2FNN) and its applications. IEEE Trans Ind Electron 61(1):447–459CrossRefGoogle Scholar
  18. Lin C-M, Le T-L, Huynh T-T (2018) Self-evolving function-link interval type-2 fuzzy neural network for nonlinear system identification and control. Neurocomputing 275:2239–2250CrossRefGoogle Scholar
  19. Malwiya R, Rai V (2015) Optimum tuning of PI controller parameter for speed control of induction motor. Int J Adv Technol Eng Res 5:21–24Google Scholar
  20. Masumpoor S, Khanesar MA et al (2015) Adaptive sliding-mode type-2 neuro-fuzzy control of an induction motor. Expert Syst Appl 42(19):6635–6647CrossRefGoogle Scholar
  21. Mendel JM, John RI, Liu F (2006) Interval type-2 fuzzy logic systems made simple. IEEE Trans Fuzzy Syst 14(6):808–821 CrossRefGoogle Scholar
  22. Miloudi A, Al-Radadi EA, DRAOU A (2007) A variable gain PI controller used for speed control of a direct torque neuro fuzzy controlled induction machine drive. Turk J Electr Eng Comput Sci 15(1):37–49Google Scholar
  23. Mohammadzadeh A, Ghaemi S (2017) Synchronization of uncertain fractional-order hyperchaotic systems by using a new self-evolving non-singleton type-2 fuzzy neural network and its application to secure communication. Nonlinear Dyn 88(1):1–19CrossRefGoogle Scholar
  24. Mohammadzadeh A, Ghaemi S, Kaynak O, Khanmohammadi S (2016) Robust \(h_\infty \)-based synchronization of the fractional-order chaotic systems by using new self-evolving nonsingleton type-2 fuzzy neural networks. IEEE Trans Fuzzy Syst 24(6):1544–1554CrossRefGoogle Scholar
  25. Mohammadzadeh A, Sabzalian MH, Zhang W (2019) An interval type-3 fuzzy system and a new online fractional-order learning algorithm: theory and practice. IEEE Trans Fuzzy Syst. CrossRefGoogle Scholar
  26. Nie M, Tan WW (2008) Towards an efficient type-reduction method for interval type-2 fuzzy logic systems. In: IEEE international conference on fuzzy systems. FUZZ-IEEE 2008. (IEEE world congress on computational intelligence). IEEE, 2008, pp. 1425–1432Google Scholar
  27. Pal A, Das S, Chattopadhyay AK (2017) An improved rotor flux space vector based MRAS for field oriented control of induction motor drives. IEEE Trans Power Electron 33(6):5131–5141CrossRefGoogle Scholar
  28. Sabzalian MH, Mohammadzadeh A, Lin S, Zhang W (2019) New approach to control the induction motors based on immersion and invariance technique. IET Control Theory Appl 13(10):1466–1472MathSciNetCrossRefGoogle Scholar
  29. Saghafinia A, Ping HW, Uddin MN (2014) Fuzzy sliding mode control based on boundary layer theory for chattering-free and robust induction motor drive. Int J Adv Manuf Technol 71(1–4):57–68CrossRefGoogle Scholar
  30. Tao C-W, Taur J-S (2007) Parallel distributed fuzzy sliding mode control for nonlinear mismatched uncertain systems. Soft Comput 11(7):607–616CrossRefGoogle Scholar
  31. Topaloglu M, Yarkin F, Kaya T (2018) Solid waste collection system selection for smart cities based on a type-2 fuzzy multi-criteria decision technique. Soft Comput 22:4879–4890CrossRefGoogle Scholar
  32. Vahedi M, Hadad Zarif M, Akbarzadeh Kalat A (2015) Speed control of induction motors using neuro-fuzzy dynamic sliding mode control. J Intell Fuzzy Syst 29(1):365–376MathSciNetCrossRefGoogle Scholar
  33. Vahedi M, Hadad Zarif M, Akbarzadeh Kalat A (2016) An indirect adaptive neuro-fuzzy speed control of induction motors. J AI Data Min 4(2):243–251zbMATHGoogle Scholar
  34. Yousef H, Hamdy M, El-Madbouly E, Eteim D (2009) Adaptive fuzzy decentralized control for interconnected mimo nonlinear subsystems. Automatica 45(2):456–462MathSciNetCrossRefGoogle Scholar
  35. Zhang T, Liu D, Yue D (2017) Rough neuron based rbf neural networks for short-term load forecasting. In: IEEE international conference on energy internet (ICEI). IEEE, pp 291–295Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of AutomationShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China
  2. 2.Department of Electrical EngineeringUniversity of BonabBonabIran
  3. 3.School of Industrial EngineeringPurdue UniversityWest LafayetteUSA

Personalised recommendations