Application of many optimizing liaisons technique for speed control with torque ripple minimization of switched reluctance motor

  • Nutan Saha
  • Sidhartha Panda
Keywords: Switch reluctance motor (SRM); Proportional integral (PI) controller; Torque ripple; Many optimizing liaison (MOL); Gravitational search algorithm (GSA)


A comparison in the performance of Many Optimizing Liaisons (MOL) and Gravitational Search Algorithm (GSA) techniques are utilised in the present work for speed control with torque ripple minimization of Switched Reluctance Motor (SRM). The control mechanism consists of two control loop (PI controller) and turn on/ turn off angle control of the 75 KW, 4-phase 8/6 SRM. The problem considered here is to obtain the operating parameter of speed controller, current controller and turn on/ turn off angle is regarded as multi objective problem for optimization with the goal of reducing torque ripple and integral square error of speed. The simulation and analysis is executed in MATLAB/SIMULINK environment. The execution evaluation of MOL and GSA is done by evaluating different statistical parameter. It is noticed that the torque ripple coefficient, ISE of speed & current are significantly reduced by MOL approach.


[1] M. R. Harris,“Comparison of design and
performance parameters in switched reluctance
and induction motors,” Fourth Int. Conf.
Electrical Machines and Drives, 303-307, 1989.[2] H. C. Lovatt, M. L McClelland, J. M.
Stephenson, “Comparative performance of singly
salient reluctance, switched reluctance and
induction motors” , Int. Conf. Electrical
Machines and Drives, 361-365, 1997.
[3] K. M. Rahman, M. Ehsami, “Performance
analysis of electric motor drives for electric and
hybrid electric vehicle applications,” IEEE
Trans. Power Electron 1, 49-56,1996
[4] W. Wu, H. C. Lovatt, J. B. Dunlop,
“Optimisation of switched reluctance motors for
hybrid electric vehicle,” Int. Conf. Power
Electronics Machines and Drives, 177-182,
[5] M. N Anwar, I. Hussain, A. V. Radun,
“Comprehensive design methodology for
switched reluctance machines,” IEEE Trans Ind
Appl. 37, 1684-92, 2001
[6] H. Majid, F. Mohammad, “Adaptive intelligent
speed control of switched reluctance motors
with torque ripple reduction,” Energy Conver
Manage 48 1028-38, 2008.
[7] I. Hussain, “Minimization of torque ripple in
SRM drives,” IEEE Trans. Ind. Electron’ 49, 28-
[8] V. P. Vujicic, “Minimization of torque ripple and
copper losses in SR drives,” IEEE Trans. Power
Electronics 27, 388-399, 2012
[9] M. Rodrigues, P. J. Costa Branco, W. Suemitsu,
“Fuzzy logic torque ripple reduction by turn-off
angle compensation for switched reluctance
motors,”IEEE Trans. Ind. Electron 48, 711-715,
[10] J. Faiz, Solani-Khosroshahi GH, “Torque
ripple reduction in switched reluctance motor by
optimal commutation strategy using a novel
reference torque,” Electr. Power Compon. Syst
30, 769-782, 2002.
[11] E.G. Shehata , “Speed sensor less torque
control of an IPSM drive with online stator
resistance estimation using reduced order EKF,”.
Electr Power Energy Syst 47, 378-86, 2013
[12] N. C. Sahoo, S. K. Panda, P. K. Dash, “A
current modulation scheme for direct torquecontrol of switched reluctance motor using fuzzy logic,” Mechatronics 10, 353-70, 2000
[13] Z. Lin, S. Reay , W. Williams, “Torque ripple reduction in switched reluctance motor drives using B-Spline neural networks,” IEEE Trans Ind Appl 2, 1445-53, 2006
[14] L.O.A.P Henriques, L. G. Rolim, W. I Suemitsu, P. J. C. J. A Dente Branco,”Torque ripple minimization in a switched reluctance drive by neuro-fuzzy compensation, IEEE Tran. Magn 36, 3592-3594, 2000
[15] L. Kalaivani, P . Subburaj, M. W. Iruthayarajan, “Speed control of switched reluctance motor with torque ripple reduction using non-dominated sorting genetic algorithm (NSGA-II),” Int. J. Electrical Power and Energy Systems 53, 69-77, 2013
[16] X. D. Xue, K. W. E Cheng, T. W. Ng, N. C. Cheung, “Multiobjective optimization design of in-wheel switched reluctance motors in electric vehicle,” IEEE Trans. Ind Electron 57, 2980-7,2010
[17] M. Balaji, V. Kamaraj, “Evolutionary computation based multi-objective optimization of switched reluctance machine,” Int. J. Electrical Power and Energy Systems 43, 63-9,2012
[18] R. K. Sahu, S. Panda, G. T. ChandraSekhar, “A novel hybrid PSO-PS optimized fuzzy PI controller for AGC in multi area interconnected power systems,” Int. J. Electrical Power and Energy Systems 64, 880-893, 2015
[19] R. K. Sahu, S. Panda , N. K Yegireddy, “A novel hybrid DEPS optimized fuzzy PI/PID controller for load frequency control of multi-area interconnected power systems,” Journal of Process Control 24: 1596-1608, 2014.
[20] S Panda, B Mohanty, P. K. Hota,“Hybrid BFOA-PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems,”Applied Soft Computing 13, 4718-4730, 2013
[21] E Rashedi, S Nezamabadi, S Saryazdi, “GS, ‘A Gravitational Search Algorithm,”, Information Sciences 179, 2232-2248, 2009.
[22] C. K. Shiva, G. Shankar, V Mukherjee, “Automatic generation control of power system using a novel quasi-oppositional harmony search algorithm,” Int. J. Electrical Power and Energy Systems 73, 787–804, 2015
[23] S Padhy, S Panda, “A hybrid stochastic fractal search and pattern search technique based cascade PI-PD controller for automatic generation control of multi-source power systems in presence of plug in electric vehicles,” CAAI Trans. Intelligence Technology, 2016
[24] P. K. Mohanty, B. K. Sahu, T. K. Pati, S. Panda, S. K. Kar, “Design and analysis of fuzzy PID controller with derivative filter for AGC in multi-area interconnected power system,”IET Generation, Transmission & Distribution 10, 3764 – 3776,2016
[25] B. K. Sahu, T. K. Pati, J. R. Nayak, S. Panda, S. K. Kar, “A novel hybrid LUS–TLBO optimized fuzzy-PID controller for load frequency control of multi-source power system,” Int. J. Electrical Power and Energy Systems 74, 58-69, 2016
[26] S. Mirzalili, S. Z. M. Hashim, “A New Hybrid PSOGSA algorithm for function optimization,” IEEE International Conference on Computer and Information Application 374-377, 2010
[27] J. Kennedy, R. C. Eberhart, “Particle swarm optimization,” In proceedings of IEEE International Conference on Neural Networks 4, 1942-1948,1995.
[28] Y. Shi, R. C Eberhart, “A modified particle swarm optimizer,” In proceedings of IEEE international conference on evolutionary computing,1998
[29] M. E. H. Pederson, A. J. Chipperfield, “Simplifying particle swarm optimization. Applied Soft Computing 10, 618-628, 2010
[30] M. Alrifai, M. Zribi, M. Rayan, R. Krishnan, “Speed control of switched reluctance motors taking into account mutual inductances and magnetic saturation effects,” Energy Conv. Manag 51,1287–97, 2010.
[31] D. Kiruthika, D .Susitra, “Speed Controller of Switched Reluctance Motor,” Indian Journal of Science and Technology’, 7 (8) 1043–1048, 2014.
[32] D. A. Torrey, X. M. Niu, E. J. Unkauf, “Analytical modeling of variable reluctance machine magnetization characteristics,” IEE Proc.-Elctr. Power Appl 142,14-22, 1995
[33] L. H. Hoang, P. Brunelle, “A versatile nonlinear switched reluctance motor in simulink using realistic and analytical magnetic characteristics,”Proc. 31st Annual Conference of IEEE Industrial Electronics Society, IECON, 2005
How to Cite
Saha, N., & Panda, S. (2018, November 2). Application of many optimizing liaisons technique for speed control with torque ripple minimization of switched reluctance motor. ASIAN JOURNAL FOR CONVERGENCE IN TECHNOLOGY (AJCT ) -UGC LISTED, 4(II). Retrieved from