Self-Tuning Fuzzy Logic Speed Control Of Induction Motor Drives

Induction motor drives are commonly applicable in various industrial applications, such as traction system, electric vehicle and home appliances. This high performance drive require robust controller to obtain satisfactory performance in terms of speed demand change, load disturbance, inertia variat...

Full description

Saved in:

Bibliographic Details
Main Author:	Farah,, Nabil Salem Yahya
Format:	Thesis
Language:	English English
Published:	2019
Subjects:	T Technology (General) TJ Mechanical engineering and machinery
Online Access:	http://eprints.utem.edu.my/id/eprint/24692/1/Self-Tuning%20Fuzzy%20Logic%20Speed%20Control%20Of%20Induction%20Motor%20Drives.pdf http://eprints.utem.edu.my/id/eprint/24692/2/Self-Tuning%20Fuzzy%20Logic%20Speed%20Control%20Of%20Induction%20Motor%20Drives.pdf
Tags:	Add Tag phd 1

id	my-utem-ep.24692
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Talib, Md Hairul Nizam
topic	T Technology (General) TJ Mechanical engineering and machinery
spellingShingle	T Technology (General) TJ Mechanical engineering and machinery Farah,, Nabil Salem Yahya Self-Tuning Fuzzy Logic Speed Control Of Induction Motor Drives
description	Induction motor drives are commonly applicable in various industrial applications, such as traction system, electric vehicle and home appliances. This high performance drive require robust controller to obtain satisfactory performance in terms of speed demand change, load disturbance, inertia variation and non-linearity. Fuzzy Logic Control (FLC) is suitable for controller design especially when the system is difficult to be modelled mathematically due to its complexity, nonlinearity and imprecision. However, FLC with fixed parameters may experience degradation when the system operates away from the design point, and encounters parameter variation or load disturbance. The purpose of this project is to design and implement Self-Tuning Fuzzy Logic Controller (ST-FLC) for Induction Motor (IM)drives. The proposed self-tuning mechanism is able to adjust the output scaling factor of the output controller for main FLC. This process enhances the accuracy of the crisp output. This research begins by designing Indirect Field Oriented Control (IFOC) method fed by Hysteresis Current Controller (HCC) induction motor drive system. The FLC with fixed parameters for the speed controller comprises 9-rules are tuned to achieve best performance. Then, a simple self-tuning mechanism is applied to the main fuzzy logic speed controller. All simulations are executed by using Simulink and fuzzy tools in MATLAB software. The effectiveness of the proposed controller is determined by conducting a comparative analysis between FLC with fixed parameters and ST-FLC over a wide range of operating conditions, either in forward and reverse operations, load disturbance or inertia variations. Finally, experimental investigation is carried out to validate the simulation results by the aid of digital signal controller board dSPACE DS1104 with the induction motor drives system. Based on the results, ST-FLC has shown superior performance in transient and steady state conditions in term of various performance measures such as overshoot, rise time, settling time and recovery time over wide speed range operation. In comparison to fixed parameter FLC, the proposed ST-FLC reduced the settling time by 40.5%, rise time by 47.3% and speed drop by 19.2%. The proposed self-tuning mechanism is relatively simpler and consumes less computational burden compared to other self-tuning methods. This is proved by measuring the computational burden of another Self-Tuning method which used fuzzy rules to tune the output scaling factor. The execution time of the proposed self-tuning found to be 0.5 x10−3 seconds compared to 1.2 x10−3 seconds for the other self-tuning.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Farah,, Nabil Salem Yahya
author_facet	Farah,, Nabil Salem Yahya
author_sort	Farah,, Nabil Salem Yahya
title	Self-Tuning Fuzzy Logic Speed Control Of Induction Motor Drives
title_short	Self-Tuning Fuzzy Logic Speed Control Of Induction Motor Drives
title_full	Self-Tuning Fuzzy Logic Speed Control Of Induction Motor Drives
title_fullStr	Self-Tuning Fuzzy Logic Speed Control Of Induction Motor Drives
title_full_unstemmed	Self-Tuning Fuzzy Logic Speed Control Of Induction Motor Drives
title_sort	self-tuning fuzzy logic speed control of induction motor drives
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Electrical Engineering
publishDate	2019
url	http://eprints.utem.edu.my/id/eprint/24692/1/Self-Tuning%20Fuzzy%20Logic%20Speed%20Control%20Of%20Induction%20Motor%20Drives.pdf http://eprints.utem.edu.my/id/eprint/24692/2/Self-Tuning%20Fuzzy%20Logic%20Speed%20Control%20Of%20Induction%20Motor%20Drives.pdf
_version_	1747834090083581952
spelling	my-utem-ep.246922021-10-05T10:15:43Z Self-Tuning Fuzzy Logic Speed Control Of Induction Motor Drives 2019 Farah,, Nabil Salem Yahya T Technology (General) TJ Mechanical engineering and machinery Induction motor drives are commonly applicable in various industrial applications, such as traction system, electric vehicle and home appliances. This high performance drive require robust controller to obtain satisfactory performance in terms of speed demand change, load disturbance, inertia variation and non-linearity. Fuzzy Logic Control (FLC) is suitable for controller design especially when the system is difficult to be modelled mathematically due to its complexity, nonlinearity and imprecision. However, FLC with fixed parameters may experience degradation when the system operates away from the design point, and encounters parameter variation or load disturbance. The purpose of this project is to design and implement Self-Tuning Fuzzy Logic Controller (ST-FLC) for Induction Motor (IM)drives. The proposed self-tuning mechanism is able to adjust the output scaling factor of the output controller for main FLC. This process enhances the accuracy of the crisp output. This research begins by designing Indirect Field Oriented Control (IFOC) method fed by Hysteresis Current Controller (HCC) induction motor drive system. The FLC with fixed parameters for the speed controller comprises 9-rules are tuned to achieve best performance. Then, a simple self-tuning mechanism is applied to the main fuzzy logic speed controller. All simulations are executed by using Simulink and fuzzy tools in MATLAB software. The effectiveness of the proposed controller is determined by conducting a comparative analysis between FLC with fixed parameters and ST-FLC over a wide range of operating conditions, either in forward and reverse operations, load disturbance or inertia variations. Finally, experimental investigation is carried out to validate the simulation results by the aid of digital signal controller board dSPACE DS1104 with the induction motor drives system. Based on the results, ST-FLC has shown superior performance in transient and steady state conditions in term of various performance measures such as overshoot, rise time, settling time and recovery time over wide speed range operation. In comparison to fixed parameter FLC, the proposed ST-FLC reduced the settling time by 40.5%, rise time by 47.3% and speed drop by 19.2%. The proposed self-tuning mechanism is relatively simpler and consumes less computational burden compared to other self-tuning methods. This is proved by measuring the computational burden of another Self-Tuning method which used fuzzy rules to tune the output scaling factor. The execution time of the proposed self-tuning found to be 0.5 x10−3 seconds compared to 1.2 x10−3 seconds for the other self-tuning. 2019 Thesis http://eprints.utem.edu.my/id/eprint/24692/ http://eprints.utem.edu.my/id/eprint/24692/1/Self-Tuning%20Fuzzy%20Logic%20Speed%20Control%20Of%20Induction%20Motor%20Drives.pdf text en public http://eprints.utem.edu.my/id/eprint/24692/2/Self-Tuning%20Fuzzy%20Logic%20Speed%20Control%20Of%20Induction%20Motor%20Drives.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=116934 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Electrical Engineering Talib, Md Hairul Nizam A. El Dessouky, M.T. and D.B., 2002. Enhanced Model Reference Fuzzy Logic Controller for High Performance Induction Motor Drive. Technical paper, Royal Military college of Canada. 1. Ahmed, H. and Rajoriya, A., 2017. A hybrid of sliding mode control and fuzzy logic control using a fuzzy supervisory switched system for DC motor speed control. Turkish Journal of Electrical Engineering & Computer Sciences, 25, pp.1993.2004. 2. Ahmed, W. and Ali, S.M.U., 2013. Comparative study of SVPWM ( space vector pulse width modulation ) & SPWM ( sinusoidal pulse width modulation ) based three phase voltage source inverters for variable speed drive. IOP Conference Series On Materials Science and Engineering, pp.0.8. 3. Alamir, M., 2002. Sensitivity Analysis in Simultaneous State/Parameter Estimation for Induction Motors. IFAC Proceedings , 35(1), pp. 211.216. 4. Allaoua, B. and Laoufi, A., 2013. A novel sliding mode fuzzy control based on SVM for electric vehicles propulsion system. Energy Procedia, 36, pp.120-129. 5. Amezquita-Brooks, L.A., Ugalde-Loo, C.E., Liceaga-Castro, E. and Liceaga-Castro, J., 2018. In-depth cross-coupling analysis in high-performance induction motor control. Journal of the Franklin Institute, 355(5), pp.2142-2178. 6. Ansuj, S., Shokooh, F. and Schinzinger, R., 1989. Parameter estimation for induction machines based on sensitivity analysis. IEEE Transactions on Industry Applications, 25(6), pp.1035.1040. 7. Areed, F.G., Haikal, A.Y. and Mohammed, R.H., 2010. Adaptive neuro-fuzzy control of an induction motor. Ain Shams Engineering Journal, 1(1), pp.71-78. 8. Asgharpour-alamdari, H., 2016. A Fuzzy-Based Speed Controller for Improvement of Induction Motor f s Drive Performance. Iranian Journal of Fuzzy Systems, 2(1), pp.61.70. 9. Bai, Y., Guo, N. and Agbegha, G., 2012. Fuzzy Interpolation and Other Interpolation Methods Used in Robot Calibrations. Journal of Robotics, 2012, pp.1.9. 10. Bai, Y., Zhuang, H. and Wang Dali, 2007. Advanced Fuzzy Logic Technologies in Industrial Applications, Taylor and francies ,2nd edition ,pp. 494-495. 11. Balamurugan, S., Venkatesh, P. and Varatharajan, M., 2017. Fuzzy sliding-mode control with low pass filter to reduce chattering effect: an experimental validation on Quanser SRIP. Sadhana - Academy Proceedings in Engineering Sciences, 42(10), pp.1693.1703. 12. Betin, F., Deloizy, M. and Goeldel, C., 1999. Closed Loop Control of a Stepping Motor Drive - Comparison between PID Control, Self-Tuning Regulation and Fuzzy Logic Control. European Power Electronics Journal, 8(2), pp. 33-39. 13. Betin, F., Pinchon, D. and Capolino, G.-A., 2000. Fuzzy logic applied to speed control of a stepping motor drive. IEEE Transactions on Industrial Electronics, 47(3), pp.610.622. 14. Bin Mohd Aras, et al, 2011. Study of the effect in the output membership function when tuning a Fuzzy logic controller. IEEE International Conference on Control System, Computing and Engineering, , pp. 1.6. 15. Blej, M. and Azizi, M., 2016. Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Fuzzy Real Time Scheduling. International Journal of Applied Engineering Research, 11(22), pp.11071.11075. 16. Bolognani, S. and Zigliotto, M., 1996. Fuzzy logic control of a switched reluctance motor drive. IEEE Transactions on Industry Applications, 32(5), pp.1063.1068. 17. Bose, B.K., 1990. An Adaptive Hysteresis-Band Current Control Technique of a Voltage-Fed PWM Inverter for Machine Drive System. IEEE Transactions on Industrial Electronics, 18. 37(5), pp.402.408. 19. Bousserhane, I.K., Hazzab, A., Rahli, M. and Kamli, M., 2007. Optimal Fuzzy Gains Scheduling of PI Controller for Induction., Acta Electrotechnica et Informatica 7.1, pp.1-10. 20. Cao, C., Ma, L. and Xu, Y., 2012. Adaptive control theory and applications. Journal of Control Science and Engineering, 2012, pp.2012.2014. 21. Chameau, J.-L. and Santamarina, J.C., 1987. Membership functions I: Comparing methods of measurement. International Journal of Approximate Reasoning, 1(3), pp.287.301. 22. Chang, J.-L., 2012. On Chattering-Free Dynamic Sliding Mode Controller Design. Journal of Control Science and Engineering, 2012, pp.1.7. 23. Chaoui, H. and Sicard, P., 2012. Adaptive fuzzy logic control of permanent magnet synchronous machines with nonlinear friction. IEEE Transactions on Industrial Electronics, 59(2), pp.1123.1133. 24. Chekkouri, M.R., Lopez, C.J., Rubira, A.E. and Martinez, R.L., 2003. Fuzzy Adaptive Control of an Induction Motor Drive. Automatika, 44, pp.113.122. 25. Chen, Y., Wei, Y., Liang, S. and Wang, Y., 2016. Indirect model reference adaptive control for a class of fractional order systems. Communications in Nonlinear Science and Numerical Simulation, 39, pp.458.471. 26. Chitra, A. et al., 2017. Performance Comparison of Multilevel Inverter Topologies for Closed Loop v/f Controlled Induction Motor Drive. Energy Procedia, 117, pp.958.965. 27. Chitra, V. and Prabhakar, R., 2006. Induction motor speed control using fuzzy logic controller. World Academy of Science and Engineering, pp.17.22. 28. Chung, H.Y., Chen, B.C. and Lin, J.J., 1998. A PI-type fuzzy controller with self-tuning scaling factors. Fuzzy Sets and Systems, 93(1), pp.23.28. 29. Cingolani, P. and Alcala-Fdez, J., 2012. FuzzyLogic: A robust and flexible Fuzzy-Logic inference system language implementation. IEEE International Conference on Fuzzy Systems, pp.10.15. 30. Cirrincione, M., Pucci, M., Cirrincione, G. and Capolino, G.A., 2003. A new experimental application of least-squares techniques for the estimation of the induction motor parameters. IEEE Transactions on Industry Applications, 39(5), pp.1247.1256. 31. Daya, J.L.F., Subbiah, V. and Sanjeevikumar, P., 2013. Robust Speed Control of an Induction Motor Drive using Wavelet-fuzzy based Self-tuning Multiresolution Controller. International Journal of Computational Intelligence Systems, 6(4), pp.37.41. 32. De, R.R. and Mudi, R.K., 2012. A robust self-tuning fuzzy controller for integrating systems. 2nd International Conference on Power Control and Embedded Systems, pp.2.7. 33. Demirbas, S., 2017. Self-tuning fuzzy-PI-based current control algorithm for doubly fed induction generator. IET Renewable Power Generation, 11(13), pp.1714.1722. 34. Deshpande, V., Chaudhari, J.G. and Jagtap, P.P., 2009. Development and Simulation of SPWM and SVPWM Control Induction Motor Drive. Second International Conference on Emerging Trends in Engineering & Technology, 2, pp.748.752. 35. Devadhas, G.G., 2012. ANN Based MARC Controller Design for an Industrial Chemical Process. International Conference on Computing, Electronics and Electrical Technologies [ICCEET), pp.375.382. 36. Dorrell, D.G., Knight, A.M., Member, S. and Evans, L., 2012. Analysis and Design Techniques Applied to Hybrid Vehicle Drive Machines . Assessment of Alternative IPM and Induction Motor Topologies. IEEE Transactions On Industrial Electronics, 59(10), pp.3690.3699. 37. Dos Santos, T.H., Goedtel, A., Da Silva, S.A.O. and Suetake, M., 2014. Scalar control of an induction motor using a neural sensorless technique. Electric Power Systems Research, 108, pp.322.330. 38. Eds, J.F.W. et al., 2011. Advances in Industrial Control, 1st edition, Springer London. 39. El-nagar, A.M. and El-bardini, M., 2017. Engineering Applications of Artificial Intelligence Parallel realization for self-tuning interval type-2 fuzzy controller. Engineering Applications of Artificial Intelligence, 61(1), pp.8.20. 40. Elbuluk, M., Langovsky, N. and David Kankam, M., 1998. Design and implementation of a closed-loop observer and adaptive controller for induction motor drives. IEEE Transactions on Industry Applications, 34(3), pp.435.443. 41. Espina, J. et al., 2009. Speed Anti-Windup PI strategies review for Field Oriented Control of Permanent Magnet Synchronous Machines. 6th International Conferene-Workshop Compatibility and Power Electronic. pp. 1-6. 42. F. Blaschke, 1972. The Principle of Field Orientation as Applied to the New Transvector Closed-Loop Control System for Rotating-Field Machines. Power Electronics, 34(3), pp.217.220. 43. Fang, G., Kwok, N.M. and Ha, Q., 2008. Automatic fuzzy membership function tuning using the particle swarm optimisation. IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application , pp. 324.328. 44. Febin Daya J.L, Subbiah, V., Iqbal, A. and Padmanaban, S., 2013. Novel Wavelet-Fuzzy Based Indirect Field Oriented Control of Induction Motor Drives. Journal of Power Electronics, 13(4), pp.656-668. 45. Feng, G., Liu, Y. and Huang, L., 2004. A New Robust Algorithm to Improve the Dynamic Performance on the Speed Control of Induction Motor Drive. IEEE Transactions on Power Electronics, 19(6), pp.1614.1627. 46. Fizatul, A.P., Marizan, S. and Zulkifilie, I., 2011. Comparison Performance of Induction Motor Using SVPWM And Hysteresis Current Controller. Journal of Theoretical and Applied Information Technology, 30(1), pp.10.17. 47. Fnaiech, M.A. et al., 2014. Model-free controller tuning based on DFT processing: Application to induction motor drives. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2(4), pp.1013.1023. 48. Franck Betin, and G.-A.C., 2007. Large, Sliding mode control for an induction machine submitted to Configuration, variations of mechanical. International Journal of Adaptive Control and Signal Processing, 22(4), pp.325.343. 49. Gadoue, S., Armstrong, M., Smith, A. and Finch, J., 2013. Improved method for the scalar control of induction motor drives. IET Electric Power Applications, 7(6), pp.487.498. 50. Gadoue, S.M., Giaouris, D. and Finch, J.W., 2010. MRAS Sensorless Vector Control of an Induction Motor Using New Sliding-Mode and Fuzzy-Logic Adaptation Mechanisms. IEEE Transactions on Energy Conversion, 25(2), pp.394.402. 51. Gdaim, S., Mtibaa, A. and Mimouni, M.F., 2015. Design and experimental implementation of DTC of an induction machine based on fuzzy logic control on FPGA. IEEE Transactions on Fuzzy Systems, 23(3), pp.644.655. 52. Gole, A.M., 2000. Sinusoidal Pulse width modulation. Power Electronics, pp.1.8. 53. Goodwin, G.C. and Mayne, D.Q., 1987. A parameter estimation perspective of continuous time model reference adaptive control. Automatica, 23(1), pp.57.70. 54. Grune, L., 2014. Encyclopedia of Systems and Control,1st edition , Springer-Verlag London pp.1-1554. 55. Guedes, J.J., Castoldi, M.F. and Goedtel, A., 2016. Temperature influence analysis on parameter estimation of induction motors using differential evolution. IEEE Latin America Transactions, 14(9), pp.4097.4105. 56. H. Stemmler, T.E., 1994. Spectral Analysis of the Sinusoidal PWM with Variable Switching Frequency for Noise Reduction in Inverter-Fed Induction Motor. 25th Annual IEEE Conference On Power Electronics Specialists, pp. 269.277. 57. Hameed, S., Das, B. and Pant, V., 2008. A self-tuning fuzzy PI controller for TCSC to improve power system stability. Electric Power Systems Research, 78(10), pp.1726.1735. 58. Hannan, M.A., Ali, J.A., Mohamed, A. and Hussain, A., 2017. Optimization techniques to enhance the performance of induction motor drives: A review. Renewable and Sustainable Energy Reviews, 81(4), pp.1611.1626. 59. Hazzab, a., Bousserhane, I.K., Zerbo, M. and Sicard, P., 2006. Real Time Implementation of Fuzzy Gain Scheduling of PI Controller for Induction Machine Control. 2nd International Conference on Information & Communication Technologies, 1(1), pp.51.60. 60. Hazzab, A., Bousserhane, I.K., Zerbo, M. and Sicard, P., 2006. Real time implementation of fuzzy gain scheduling of PI controller for induction motor machine control. Neural Processing Letters, 24(3), pp.203.215. 61. He, R.B., Zheng, S.J. and Wang, H.T., 2013. Independent modal variable structure fuzzy active vibration control of cylindrical thin shells laminated with photostrictive actuators. Shock and Vibration, 20(4), pp.693.709. 62. Hernandez-Guzman, V.M. and Santibanez, V., 2012. A Saturated PI Velocity Controller for Voltage-Fed Induction Motors. European Journal of Control, 18(1), pp.58.68. 63. Hinkkanen, M. and Luomi, J., 2003. Parameter Sensitivity of Full-Order Flux Observers for Induction Motors. IEEE Transactions on Industry Applications, 39(4), pp.1127.1135. 64. Ho, C.N.M., Cheung, V.S.P. and Chung, H.S.H., 2009. Constant-frequency hysteresis current control of grid-connected VSI without bandwidth control. IEEE Transactions on Power Electronics, 24(11), pp.2484.2495. 65. Holtz, J., 1992. Pulse width Modulation.A Survey. IEEE Transactions on Industrial Electronics, 39(5), pp.410.420. 66. Ibrahim, Z. and Levi, E., 2002. A comparative analysis of fuzzy logic and PI speed control in high-performance AC drives using experimental approach. IEEE Transactions on Industry Applications, 38(5), pp.1210-1218. 67. Iqbal, A., Abu-Rub, H. and Nounou, H., 2014. Adaptive fuzzy logic-controlled surface mount permanent magnet synchronous motor drive. Systems Science & Control Engineering., 2(1), pp.465.475. 68. Islam, M., Raju, N. and Ahmed, A., 2013. Sinusoidal PWM Signal Generation Technique for Three Phase Voltage Source Inverter with Analog Circuit & Simulation of PWM Inverter for Standalone Load & Micro. International Journal of Renewable Energy, 3(3), pp.647-658. 69. Ismail, B. et al., 2006. Development of a single phase SPWM microcontroller-based inverter. First International Conference on Power and Energy, (PECon ), pp.437.440. 70. Jacob, J.T. and Kirubakaran, D., 2017. Modelling and Simulation of a Fuzzy Logic Controller for State of Charge Monitoring and Control Integrated with a Novel Spike Current Method. International Journal of Control Theory and Applications, 10(10), pp.207.219. 71. Jain, J.K., Ghosh, S., Maity, S. and Dworak, P., 2017. PI controller design for indirect vector controlled induction motor: A decoupling approach. ISA Transactions, 70, pp.378.388. 72. Jain, S., Thopukara, A.K., Karampuri, R. and Somasekhar, V.T., 2015. A single-stage photovoltaic system for a dual-inverter-fed open-end winding induction motor drive for pumping applications. IEEE Transactions on Power Electronics, 30(9), pp.4809.4818. 73. Janaszek, M., 2016. Structures of vector control of n-phase motor drives based on generalized Clarke transformation. Bulletin of The Polish Academy Of Sciences Technical Sciences, 64(4), pp.865-872. 74. Johnson, C.R., 1980. Adjustable Model Reference Adaptive Control Reconfiguration of Self-Tuning Pole Replacement. 18th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes ,Vol. 2, pp. 359-360. 75. Kalyanraj, D. and Lenin Prakash, S., 2014. Design and digital implementation of constant frequency hysteresis current controller for three-phase voltage source inverter using TMS320F2812. International Journal of Emerging Electric Power Systems, 15(1), pp.13.23. 76. Khan, W. and Taylor, D.G., 1999. Adaptive control of ac motor drives with inverter non-linearities. International Journal of Control, 72(9), pp.784.798. 77. Kim, E., Park, M., Ji, S. and Park, M., 1997. A new approach to fuzzy modeling. IEEE Transactions on Fuzzy Systems, 5(3), pp.328.337. 78. Kohlrusz, G. and Fodor, D., 2011. Comparison of Scalar and Vector Control Strategies of Induction Motors. Hungarian Journal of Industrial Chemistry Veszprem, 39(2), pp.265.270. 79. Komurcugil, H., Bayhan, S. and Abu-Rub, H., 2017. Variable-and Fixed-Switching-Frequency-Based HCC Methods for Grid-Connected VSI with Active Damping and Zero Steady-State Error. IEEE Transactions on Industrial Electronics, 64(9), pp.7009.7018. 80. Kriauciunas, J., Rinkeviciene, R. and Baskys, A., 2014. Self-Tuning Speed Controller of the Induction Motor Drive. Electronics & Electrical Engineering, 20(6), pp.24.28. 81. Kucer, P. and Dobrucky, B., 1994. Robust Fuzzy-Logic Control of Converter - Synchronous Motor Drive. IFAC Proceedings , 27(11), pp.257.262. 82. Kumar, B., Chauhan, Y.K. and Shrivastava, V., 2012. Efficacy of Different Rule Based Fuzzy Logic Controllers for Induction Motor Drive. International Journal of Machine Learning and Computing, 2(2), pp.131.137. 83. Kumar Behera, P., Behera, M.K. and Sahoo, A.K., 2014. Comparative Analysis of scalar & vector control of Induction motor through Modeling & Simulation. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, 2(4), pp.2321.2004. 84. Kumar, K.V., 2016. Chattering Free Sliding mode Controller for Load Frequency Control of Multi Area Power System in Deregulated Environment. IEEE 7th International Conference on Power, 1(2), pp.1.5. 85. Kung, Y.S., Liaw, C.M. and Ouyang, M.S., 1995. Adaptive speed control for induction motor drives using neural networks. IEEE Transactions on Industrial Electronics, 42(1), pp.25.32. 86. Kung, Y.S. and Tsai, M.H., 2007. FPGA-based speed control IC for PMSM drive with adaptive fuzzy control. IEEE Transactions on Power Electronics, 22(6), pp.2476.2486. 87. Lai, M.F., Nakano, M. and Hsieh, G.C., 1996. Application of fuzzy logic in the phase-locked loop speed control of induction motor drive. IEEE Transactions on Industrial Electronics, 43(6), pp.630.639. 88. Lee, J., Sathiakumar, S. and Shrivastava, Y., 2013. A new integration method to estimate stator flux in induction motor with dc offset error. IEEE Tencon-Spring Conference Proceedings, pp.75.79. 89. Li, H. and Curiac, R.S., 2010. Designing more efficient large industrial induction motors by utilizing the advantages of adjustable-speed drives. IEEE Transactions on Industry Applications, 46(5), pp.1805.1809. 90. Li, J. and Zhong, Y., 2015. Robust speed control of induction motor drives employing first-order auto-disturbance rejection controllers. Annual Meeting (IEEE Industry Applications Society), 51(1), pp.712.720. 91. Li, Y.W., Pande, M., Zargari, N.R. and Wu, B., 2010. An Input Power Factor Control Strategy for High-Power Current-Source Induction Motor Drive With Active Front-End. IEEE Transactions on Power Electronics, 25(2), pp.352.359. 92. Lim, C.S. et al., 2013. FCS-MPC Based Current Control of a Five- Phase Induction Motor and its Comparison with PI-PWM Control. IEEE Transactions on Industrial Electronics, 61(1), pp.149-163. 93. Lokriti, A., Salhi, I., Doubabi, S. and Zidani, Y., 2013. Induction motor speed drive improvement using fuzzy IP-self-tuning controller. A real time implementation. ISA Transactions, 52(3), pp.406.417. 94. Mahmoud, A.A., Ortmeyer, T.H., Harley, R.G. and Calabrese, C., 1980. Effects of reactive compensation on induction motor dynamic performance. IEEE Transactions on Power Apparatus and Systems, (3), pp.841-846. 95. Mamdani, E.H., 1977. Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis. IEEE Transactions on Computers, C-26(12), pp.1182.1191. 96. Mamdani, E.H. and Assilian, S., 1975. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), pp.1.13. 97. Masiala, M., 2008. Fuzzy self-tuning speed control of an indirect field-oriented control induction motor drive. IEEE Transactions on Industry Applications, 44(6), pp.1732.1740. 98. Masiala, M., 2010. Self-Tuned Indirect Field Oriented Controlled IM Drive, Doctor of Philosophy Power Engineering and Power Electronics. University of Alberta. 99. Melba Mary, P. and Marimuthu, N.S., 2009. Design of self-tuning fuzzy logic controller for the control of an unknown industrial process. IET Control Theory & Applications, 3(4), pp.428.436. 100. Menghal, P.M. and Laxmi, A.J., 2016. Fuzzy Based Real Time Control of Induction Motor Drive. Procedia Computer Science Elsevier Masson SAS, pp. 228.235. 101. Merabet, E., Amimeur, H., Hamoudi, F. and Abdessemed, R., 2011. Self . Tuning Fuzzy Logic Controller for a Dual Star Induction Machine. Journal of Electrical Engineering & Technology, 6(1). 102. Milotid, Y., Miloudi, A. and Mostefai, M., 2004. Self-Tuning Fuzzy Logic Speed Controller for Induction Motor Drives. IEEE International Conference on Industrial Technology,. (Vol. 1, pp. 454-459) 103. Mohammad Abdul Mannan, Toshiaki Murata, J.T., 2013. A Fuzzy Logic Controller with Tuning Output Scaling Factor for Induction Motor Control Taking Core Loss into Account. International Journal of Intelligent Systems and Applications in Engineering, 4(8), pp.1166.1169. 104. Mohammadian, M. and R. J. Stonier, 1994. Tuning and optimisation of membership functions of fuzzy logic controllers by genetic algorithms. IEEE International Workshop on Robot and Human Communication, pp.356.361. 105. Mohapatra, M. and Babu, B.C., 2010. Fixed and sinusoidal-band hysteresis current controller for PWM voltage source inverter with LC filter. IEEE Students Symposium On Technology. pp. 88.93. 106. Mokrani, L., 2004. Influence Of Fuzzy Adapted Scaling Factors on The Performance of A Fuzzy Logic Controller Based On An Indirect Vector Control For Induction Motor Drive. Journal of Electrical Engineering, 55(7), pp.188.194. 107. Mokrani, L. and Abdessemed, R., 2003. A fuzzy self-tuning PI controller for speed control of induction motor drive. IEEE Conference on Control Applications, 2, pp.785.790. 108. Nasir Uddin, M., Huang, Z.R. and Siddique Hossain, A.B.M., 2014. Development and implementation of a simplified self-tuned neuro-fuzzy-based im drive. IEEE Transactions on Industry Applications, 50(1), pp.51.59. 109. Olivares, M. and Sala, A., 1999. Fuzzy Logic Based Look-Up Table Regulator. EUSFLAT-ESTYLF Joint Conference, (1), pp.22.25. 110. Orlowska-Kowalska, T., Dybkowski, M. and Szabat, K., 2010. Adaptive sliding-mode neuro-fuzzy control of the two-mass induction motor drive without mechanical sensors. IEEE Transactions on Industrial Electronics, 57(2), pp.553.564. 111. Orlowska-Kowalska, T. and Szabat, K., 2004. Optimization of fuzzy-logic speed controller for DC drive system with elastic joints. IEEE Transactions on Industry Applications, 40(4), pp.1138.1144. 112. Ozger, M., 2009. Comparison of fuzzy inference systems for streamflow prediction. Hydrological Sciences Journal, 54(2), pp.261.273. 113. Pal, A.K. and Mudi, R.K., 2008. Self-Tuning Fuzzy PI Controller and its Application to HVAC Systems. International Journal of Computational Cognition, 6(1), pp.25.30. 114. Parida, P., 2009. a Sliding Mode Controller for Induction Motor Drives. Electrical Engineering. (Doctoral dissertation, National Institute Of Technology). 115. Patakor, Fizatul Aini, Marizan Sulaiman, and Z.I., 2011. Comparison Performance of Induction Motor Using Svpwm And Hysteresis Current. Journal of Theoretical and Applied Information Technology, 30(1). 116. Patil, S., Bhaskar, P. and Sudheer, S., 2011. Design and Implementation of an Integrated Fuzzy Logic Controller for a Multi-input Multi-output System. Defence Science Journal, 61(3), pp.219.227. 117. Pattnaik, A. et al., 2014. Design and Implementation of SPWM and Hysteresis based VSI Fed Induction Motor. Emerging Energy Technology perspectives-A Sustainable Approach, pp.17.24. 118. Perdukova, D. and Fedor, P., 2014. A Model-Based Fuzzy Control of an Induction Motor. Advances in Electrical and Electronic Engineering, 12(5), pp.427.434. 119. Pham, C. et al., 2012. Self-Tuning Fuzzy PI-Type Controller in Z-Source Inverter for Hybrid Electric Vehicles. International Journal of Power Electronics and Drive System (IJPEDS), 2(4), pp.353.363. 120. Pillay, P. and Krishnan, R., 1989. Modeling, simulation, and analysis of permanent-magnet motor drives. I. The permanent-magnet synchronous motor drive. IEEE Transactions on Industry Applications , 25(2), pp.265.273. 121. Pohl, L. and Vesely, I., 2016. Speed Control of Induction Motor Using H‡ Linear Parameter Varying Controller. IFAC-Papers OnLine, 49(25), pp.74.79. 122. Poulsen, S. and Andersen, M.A.E., 2005. Hysteresis controller with constant switching frequency. IEEE Transactions on Consumer Electronics, 51(2), pp.688.693. 123. Rafa, S. et al., 2014. Implementation of a new fuzzy vector control of induction motor. ISA Transactions, 53(3), pp.744.754. 124. Rahman, M.A., Uddin, M.N. and Abido, M.A., 2006. An Artificial Neural Network for Online Tuning of Genetic Algorithm . Based PI Controller for Interior Permanent Magnet Synchronous Motor Drive. Canadian Journal of Electrical and Computer Engineering, 31(3), pp.159.165. 125. Ramana, P., Kumar, B., Mary, K. and Kalavathi, M., 2013. Comparison Of Various PWM Techniques For Field Oriented Control VSI Fed PMSM Drive. International journal of advanced research in electrical, electronics and instrumentation engineering, 2(7), pp.2928.2936. 126. Ramchand, R. et al., 2010. Improved switching frequency variation control of hysteresis controlled voltage source inverter-fed IM drives using current error space vector. IET Power Electronics, 3(2), pp.219-231. 127. Rashid, M.H., 2007. Power Electronics Handbook, 2nd Edition, Elsevier. 128. Rebeiro, R.S. and Uddin, M.N., 2012. Performance analysis of an FLC-based online adaptation of both hysteresis and PI controllers for IPMSM drive. IEEE Transactions on Industry Applications, 48(1), pp.12.19. 129. Reddy, B.V. and Somasekhar, V.T., 2016. An SVPWM Scheme for the Suppression of Zero-Sequence Current in a Four-Level Open-End Winding Induction Motor Drive With Nested Rectifier-Inverter. IEEE Transactions on Industrial Electronics, 63(5), pp.2803.2812. 130. Rubaai, A., Ricketts, D. and Kankam, M.D., 2002. Laboratory implementation of a microprocessor-based fuzzy logic tracking controller for motion controls and drives. IEEE Transactions on Industry Applications, 38(2), pp.448.456. 131. Rui, W., Jin, Z., Huan, L. and Tao, G., 2011. A Mixed MRAC for Adaptation of Rotor Time Constant of Induction Motor based on the Parameter Sensitivity Analysis. Information Technology Journal, 10(3), pp.511.521. 132. Saidur, R. et al., 2012. Applications of variable speed drive ( VSD ) in electrical motors energy savings. Renewable and Sustainable Energy Reviews, 16(1), pp.543.550. 133. Salman Mohagheghi et al, 2009. Hardware implementation of a mamdani fuzzy logic controller for a static compensator in a multimachine power system. IEEE Transactions on Industry Applications, 45(4), pp.1535.1544. 134. Santos, T.H., Goedtel, A., Silva, S.A.O. and Suetake, M., 2012. An ANN Strategy Applied to Induction Motor Speed Estimator in Closed-Loop Scalar Control. International Conference on Electrical Machines, pp.844.850. 135. Shama, P., Dalal, A., Meshram, P. and Lanjewar, S., 2015. Space Vector Pulse width Modulation Technique based VSI Fed Three Phase Induction Motor Modelling. International Journal of Electronics, Electrical and Computational System, 5(4), pp.78.86. 136. Sheroz Khan, Salami Femi Abdulazeez, Lawal Wahab Adetunji, A.Z.A., 2009. Design and Implementation of an Optimal Fuzzy Logic Controller Using Genetic Algorithm. Journal of Computer Science, 4(10), pp.799.806. 137. Shivakumar, E.G. et al., 2002. Space Vector PWM Control of Dual Inverter Fed Open-End Winding Induction Motor Drive. IEEE Applied Power Electronics Conference and Exposition, 8368(2), pp.399.405. 138. Sousa, G.C.D., Bose, B.K. and Cleland, J.G., 1995. Fuzzy Logic Based On-Line Efficiency Optimization Control of an Indirect Vector-Controlled Induction Motor Drive. IEEE Transaction on Industry. Electronics., 42(2), pp.192.198. 139. Sudheer, H., Kodad, S.F. and Sarvesh, B., 2017. Improvements in direct torque control of induction motor for wide range of speed operation using fuzzy logic. Journal of Electrical Systems and Information Technology.,5(3),pp.813-828. 140. Suetake, M., Da Silva, I.N. and Goedtel, A., 2011. Embedded DSP-based compact fuzzy system and its application for induction-motor V/f speed control. IEEE Transactions on Industrial Electronics, 58(3), pp.750.760. 141. Sugeno, M., 1985. An introductory survey of fuzzy control. Information Sciences, 36(1.2), pp.59.83. 142. Syam, P., Kumar, R., Das, S. and Chattopadhyay, A.K., 2015. Review on model reference adaptive system for sensorless vector control of induction motor drives. IET Electric Power Applications, 9(7), pp.496.511. 143. Syed, F.U. et al., 2009. Fuzzy gain-scheduling proportional-integral control for improving engine power and speed behavior in a hybrid electric vehicle. IEEE Transactions on Vehicular Technology, 58(1), pp.69.84. 144. Szabat, K., Orlowska-Kowalska, T. and Dybkowski, M., 2009. Indirect Adaptive Control of 145. Induction Motor Drive System With an Elastic Coupling. IEEE Transactions on Industrial Electronics, 56(10), pp.4038.4042. 146. Takagi, T. and Sugeno, M., 1985. Fuzzy identification of systems and its applications to modeling and control. Systems, IEEE Transactions on Man and Cybernetics , 15(1), pp.116.132. 147. Takahashi, I. and Noguchi, T., 1986. A New Quick-Response and High-Efficiency Control Strategy of an Induction Motor. IEEE Transactions on Industry Applications, IA-22(5), pp.820.827. 148. Talib, M. and Isa, Snm., 2016. Hysteresis Current Control of Induction Motor Drives Using dSPACE DSP Controller. EEE International Conference on Power and Energy (PECon) , pp.522.527. 149. Talib, M.H.N. et al., 2014. Performance Improvement of Induction Motor Drive Using Simplified FLC Method. 16th International Power Electronics and Motion Control Conference and Exposition, pp.707.712. 150. Talib, M.H.N. et al., 2016. Simplified Self-Tuning Fuzzy Logic Speed Controller for Induction Motor Drive. IEEE International Conference on Power and Energy (PECon). 2016 Melaka, Malaysia, pp. 188.193. 151. Tam, Y. and Anwari, M., 2007, November. Comparative study of sinusoidal pulse width and hysteresis modulations in current source inverter. International Conference on Intelligent and Advanced Systems,.pp. 906-911. 152. Teja, A.V.R., Chakraborty, C., Maiti, S. and Hori, Y., 2012. A new model reference adaptive controller for four quadrant vector controlled induction motor drives. IEEE Transaction on Industry. Electronics., 59(10), pp.3757.3767. 153. Ting, N.S., 2015. Comparison of SVPWM , SPWM and HCC Control Techniques in Power Control of PMSG used in Wind Turbine Systems. International Conference on Electrical Machines & Power Electronics, pp. 69.74. 154. Toliyat, H.A., Levi, E. and Raina, M., 2003. A Review of RFO Induction Motor Parameter Estimation Techniques. IEEE Power Engineering Review, 22(7), pp. 271 - 283. 155. Tripathi, S.M., Mishra, A. and Pandey, A.K., 2017. High performance speed tracking of CSI-fed SCIM drive employing a variable-gain proportional-integral (VGPI) speed controller. Journal of Electrical Systems and Information Technology, pp.1.18. 156. Trzynadlowski, a. M. et al., 1994. Random pulse width modulation techniques for converter-fed drive and systems-a review. IEEE Transactions on Industry Applications, 30(5), pp.1166.1175. 157. Trzynadlowski, A.M., 2001. Control of Induction Motors, Elsevier. 158. Uddin, M. and Hafeez, M., 2012. FLC-based DTC scheme to improve the dynamic performance of an im drive. IEEE Transactions on Industry Applications, 48(2), pp.823.831. 159. Uddin, M., Radwan, T. and Azizur, R., 2002. Performances of fuzzy-logic-based indirect vector control for induction motor drive. IEEE Transactions on Industry Applications, 38(5), pp.1219.1225. 160. Uddin, M.N., 2011. development And Implementtation of a simplified self-tuned Neuro-Fuzzy Based IM drive. IEEE International Conference on Power Electronic & Drive System, 9(11), pp.1.7. 161. Uddin, M.N. and Wen, H., 2007. Development of a self-tuned neuro-fuzzy controller for induction motor drives. IEEE Transactions on Industry Applications, 43(4), pp.1108.1116. 162. Utkin, V. and Lee, H., 2006. Chattering Problem in Sliding Mode Control Systems. IEEE International Workshop on Variable Structure Systems. pp. 346-350. 163. Venkataramana Naik, N., Panda, A. and Singh, S.P., 2016. A Three-Level Fuzzy-2 DTC of Induction Motor Drive Using SVPWM. IEEE Transactions on Industrial Electronics, 63(3), pp.1467.1479. 164. Venkataramana Naik, N. and Singh, S.P., 2015. A Comparative Analytical Performance of F2DTC and PIDTC of Induction Motor Using DSPACE-1104. IEEE Transactions on Industrial Electronics, 62(12), pp.7350.7359. 165. Victor, J. and Dourado, A., 2007. Adaptive scaling factors algorithm for the fuzzy logic controller. IEEE 6th International Conference on Fuzzy Systems., pp. 1021.1026. 166. Wai, R.J., 2007. Fuzzy sliding-mode control using adaptive tuning technique. IEEE Transactions on Industrial Electronics, 54(1), pp.586.594. 167. Wang, S.-C. and Liu, Y.-H., 2011. A Modified PI-Like Fuzzy Logic Controller for Switched Reluctance Motor Drives. IEEE Transactions on Industrial Electronics, 58(5), pp.1812.1825. 168. Woosuk Sung, Jincheol Shin and Yu-seok Jeong, 2012. Energy-Efficient and Robust Control for High-Performance Induction Motor Drive With an Application in Electric Vehicles. IEEE Transactions on Vehicular Technology, 61(8), pp.3394.3405. 169. Yang, Z., Shang, F., Brown, I.P. and Krishnamurthy, M., 2015. Comparative Study of Interior Permanent Magnet, Induction, and Switched Reluctance Motor Drives for EV and HEV Applications. IEEE Transactions on Transportation Electrification, 1(3), pp.245.254. 170. Yousif Yacoub, A.H., Buyamin, S. and Abdul Wahab, N., 2011. Integral Time Absolute Error Minimization For Pi Controller On Coupled.Tank Liquid Level Control System Based On Stochastic Search Echniques. Journal Technology, 54(1), pp.381.402. 171. Yu, H. and Chen, Z., 2015. Three-Phase Induction Motor DTC-SVPWM Scheme with Self-tuning PI-Type Fuzzy Controller. International Journal of Computer and Communication Engineering, 4(3), pp.204.210. 172. Yu, X. and Kaynak, O., 2009. Sliding-mode control with soft computing: A survey. IEEE Transactions on Industrial Electronics, 56(9), pp.3275.3285. 173. Zadeh, L. a., 1965. Fuzzy sets. Information and Control, 8(3), pp.338.353. 174. Zadeh, L.A., 1973. Outline of a new approach to the analysis of complex systems and decision processes Systems. IEEE Transactions on , Man and Cybernetics (1), pp.28.44. 175. Zaky, M.S., 2015. A self-tuning PI controller for the speed control of electrical motor drives. Electric Power Systems Research, 119, pp.293.303. 176. Zaky, M.S. and Metwaly, M.K., 2017. A Performance Investigation of a Four-Switch Three-Phase Inverter-Fed im Drives at Low Speeds Using Fuzzy Logic and PI Controllers. IEEE Transactions on Power Electronics, 32(5), pp.3741.3753. 177. Zbede, Y., Gadoue, S. and Atkinson, D., 2016. Model Predictive MRAS Estimator for Sensorless Induction Motor Drives. IEEE Transactions on Industrial Electronics, PP(99), pp.1.1. 178. Zeraoulia, M., Benbouzid, M.E.H. and Diallo, D., 2006. Electric motor drive selection issues for HEV propulsion systems: A comparative study. IEEE Transactions on Vehicular technology, 55(6), pp.1756-1764. 179. Zhang, D., Li, H. and Collins, E.G., 2006. Digital anti-windup PI controllers for variable-speed motor drives using FPGA and stochastic theory. IEEE Transactions on Power Electronics, 21(5), pp.1496.1501. 180. Zhang, X., 2013. Sensorless Induction motor drive using indirect vector controller and sliding-mode observer for electric vehicles. IEEE Transactions on Vehicular Technology, 62(7), pp.3010.3018. 181. Zhao, J. and Bose, B.K., 2002. Evaluation of Membership Functions for Fuzzy Logic Controlled Induction Motor Drive. IEEE 28th Annual Conference of the Industrial Electronics Society, pp.229.234. 182. Zhao, J. and Bose, B.K., 2003. Membership Function Distribution Effect on Fuzzy Logic Controlled Induction Motor Drive. 29th Annual Conference on Industrial Electronics Society 1, pp.214.219. 183. Zhen, L. and Xu, L., 2000. Fuzzy learning enhanced speed control of an indirect field-oriented induction machine drive. IEEE Transactions on Control Systems Technology, 8(2), pp.270.278. 184. Zheng, L., 1992. A practical guide to tune of proportional and integral (PI) like-fuzzy controllers. IEEE International Conference on Fuzzy Systems, pp.633.640.

Self-Tuning Fuzzy Logic Speed Control Of Induction Motor Drives

Similar Items