Paper:

# Speed-Sensorless Vector Control Based on ANN MRAS for Induction Motor Drives

## Kai Xu and Shanchao Liu

College of Information Science and Engineering, University of Chongqing Jiaotong, No. 66, Xuefu Rd., Nan’an District, Chonqing 400074, China

In the speed-sensorless induction motor drives system, Model Reference Adaptive System (MRAS) is the most common strategy. However, speed estimation using reactive power based MRAS has the problem of instability in the regenerating mode of operation. Such estimation technique is simple and has several notable advantages, but is not suitable for induction motor drives. To overcome these problems, a suitable Artificial Neural Networks (ANN) is presented to replace the adjustable model to make the system stable when working at low speed and zero crossing. Simultaneously, in order to enhance the ANN convergence speed and avoid the trap of local minimum value of algorithm, we used themodified Particle Swarm Optimization (PSO) to optimize the weights and threshold values of neural networks. Then the ANN-based MRAS was used to identify the speed of motor in the indirect vector control system. The results of the simulation show that, by this method, the speed of motor can be identified accurately in different situations, and the result is reliable.

*J. Adv. Comput. Intell. Intell. Inform.*, Vol.19, No.1, pp. 127-133, 2015.

- [1] J. Holtz and J. Quan, “Drift and parameter compensated flux estimator for persistent zero stator frequency operation of sensorless controlled induction motors,” IEEE Trans. Industry Applications, Vol.39, No.4, pp. 1052-1060, 2003.
- [2] P. Vas, “Sensorless vector and direct torque control,” Oxford University Press, 1998.
- [3] Y. Yusof and A. H. M. Yatim, “Simulation and modelling of stator flux estimator for induction motor using artificial neural network technique,” Proc. of National Power and Energy Conf. pp. 11-15, 2003.
- [4] B. Karanayil, M. F. Rahman, and C. Grantham, “An implementation of a programmable cascaded low-pass filter for a rotor flux synthesizer for an induction motor drive,” IEEE Trans. on Power Electron., Vol.19, No.2, pp. 257-263, 2004.
- [5] Q. Gao, C. S. Staines, G. M. Asher, and M.Sumner, “Sensorless speed operation of cage induction motor using zero drift feedback integration with MRAS observer,” Proc. European Conf. on Power Electronics and Applications, 2005.
- [6] M. Rashed and A. F. Stronach, “A stable back-EMF MRAS-based sensorless low speed induction motor drive insensitive to stator resistance variation,” IEEE Proc. Electr. Power Appl., Vol.l51, No.6, pp. 685-693, 2004.
- [7] S. Maiti, C. Chakraborty, Y. Hori, and M. C. Ta, “Model reference adaptive controller-based rotor resistance and speed estimation techniques for vector controlled induction motor drive utilizing reactive power,” IEEE Trans. Ind. Electron., Vol. 55, No.2, pp. 594-601, 2008.
- [8] S. Maiti and C. Chakraborty, “Experimental validation of very-low and zero speed operation of a flux-eliminated adaptive estimator for vector controlled IM drive,” Proc. Conf. Rec. IEEE ICIT, pp. 1-6, 2009.
- [9] M. Hinkkanen, L. Harnefors, and J. Luomi, “Reduced-order flux observers with stator-resistance adaptation for speed-sensorless induction motor drives,” IEEE Trans. Power Electron., Vol.25, No.5, pp.1173-1183, 2010.
- [10] A. V. R. Teja, C. Chakraborty, S. Maiti, and Y.Hori, “A new model reference adaptive controller for four quadrant vector controlled induction motor drives,” IEEE Trans. Ind. Electron., Vol.59, No.10, pp. 3757-3767, 2012.
- [11] Y. Sayouti, A. Abbou, M. Akherraz, and H.Mahmoudi, “Sensorless low speed control with ANN MRAS for direct torque controlled induction motor drive,” Proc. of the 2011 Int. Conf. on Power Engineering, Energy and Electrical Drives, pp. 623-628, 2011.
- [12] A. Accetta, M. Cirrincione, M. Pucci, and G. Vitale, “Sensorless control of PMSM fractional horsepower drives by signal injection and neural adaptive-band filtering,” IEEE Trans. on Industrial Electronics, Vol.59, pp. 1355-1366, 2012.
- [13] W. Gao and Z. Guo, “Speed sensorless control of PMSM using model reference adaptive system and RBFN,” J. of networks, Vol.8, No.1, pp. 213-219, 2013.
- [14] T. Orlowska-Kowalska and M. Kaminski, “FPGA implementation of the multilayer neural network for the speed estimation of the two-mass drive system,” IEEE Trans. Ind. Informat., Vol.7, No.3, pp. 436-445, 2011.
- [15] S. M. Gadoue, D. Giaouris, and J. W. Finch, “A neural network based stator current MRAS observer for speed sensorless induction motor drives,” Proc. Industrial Electronics, pp. 650-655, 2008.
- [16] K. Xu and X. Pingwang, “Speed sensorless vector control with wavelet neural network for induction motor drive,” Proc. ICIC Express Letters, Vol.8, No.9, pp. 2431-2436, 2014.
- [17] Y. Xiaoting and Z. Qingchun, “Speed estimation of induction motor based on neural network,” Proc. of the 2nd Int. Conf. on Intelligent Control and Information Processing, pp. 619-623, 2011.
- [18] C. Wenhao and C. Jiangxia, “Speed identification method in direct torque control of asynchronous machine based on neuron network theory,” Proc. of the 2012 Int. Conf. on Computer Application and System Modeling, pp. 133-136, 2012.
- [19] Z.Wenli and L. Guorong, “Research of speed observer based on BP neural network optimized by Genetic Algorithm,” Proc. Computer Engineering and Applications, Vol.49, No.12, pp. 259-266, 2013.
- [20] S. Maiti and Y. Hori, “An dadptive speed sensorless induction motor drive with artificial neural network for stability enhancement,” IEEE Trans. on Industrial Informatics, Vol.8, No.4, pp.757-766, 2012.