A robust control of a class of induction motors using rough type-2 fuzzy neural networks
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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.
KeywordsInduction 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.
This article does not contain any studies with human participants or animals performed by any of the authors.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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