Emotional Learning Based Intelligent Controllers for Rotor Flux Oriented Control of Induction Motor

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Abstract

This paper presents design and evaluation of a novel approach based on emotional learning to improve the speed control system of rotor flux oriented control of induction motor. The controller includes a neuro-fuzzy system with speed error and its derivative as inputs. A fuzzy critic evaluates the present situation, and provides the emotional signal (stress). The controller modifies its characteristics so that the critics stress is reduced. The comparative simulation results show that the proposed controller is more robust and hence found to be a suitable replacement of the conventional PI controller for the high performance industrial drive applications.

[1] F. BLASCHKE: The Principle of Filed Orientation as Applied to The New Transvector Closed-Loop Control System for Rotat-ing-Field Machines, Siemens Review 34 No. 3 (1972), 217–220.

[2] SUGIMOTO, H.—TAMAI, S.: Secondary Resistance Identification of an Induction Motor Applied Model Reference Adaptive System and its Characteristics, IEEE Trans. On Ind. Appl. 23 No. 2 (March/April 1987), 296–303.

[3] WON, CY.—BOSE, B. K.: An Induction Motor Servo System with Improved Sliding Mode Control, IEEE Conf. Rec. Of IECON’92 (1992), 60–66.

[4] CHERN, T. L.—WU, Y. C.: Design of Integral Variable Structure Controller and Application to Electrohydraulic Velocity Servo Systems, IEE Proceedings 138 No. 5 (September 1991), 439–444.

[5] HUNG, J. C.: Practical Industrial Control Techniques, IEEE Conf. Rec. of IECON’94 (1994), 7-14. [6] ZADEH, L. A.: Fuzzy Sets, Information and Control 8 (1965), 338–353.

[7] BOLOGNANI, S.—ZIGLIOTTO, M.: Hardware and Software Effective Configurations for Multi-Input Fuzzy Logic Controllers, IEEE Trans, on Fuzzy Systems 6 No. 1 (Feb. 1998), 173–179.

[8] MIKI, I.—NAGAI, N.—NISHIYAMA, S.—YAMADA, T.: Vector Control of Induction Motor with Fuzzy PI Controller, IEEE/IAS Annual Meeting Conference Record (1991), 341–346.

[9] MIR, []S. A.—ZINGER, D. S.: Fuzzy Controller for Inverter Fed Induction Machines, IEEE/IAS Annual Meeting Conference Record (1992), 464–471.

[10] TANG, Y.—XU, L.: Fuzzy Logic Application for Intelligent Control of a Variable Speed Drive, IEEE Trans, on Energy Conversion 9 No. 4 (Dec. 1994), 679–685.

[11] CERRUTO, E.—CONSOLI, A.—RACITI, A.—TESTA, A.: Fuzzy Adaptive Vector Control of Induction Motor Drives, IEEE Trans, on Power Electronics 12 No. 6 (Nov. 1997), 1028–1039.

[12] FAKHRAZARI, A.—BOROUSHAKI, M.: Adaptive Critic-basec Neurofuzzy Controller for the Steam Generator Water Level, IEEE Transactions On Nuclear Science 55 No. 3 (JUNE 2008), 1678–1685.

[13] JAFARZADEH, S.—MIRHEIDARI, R.—MOTLAGH, M. R. J.—BARKHORDARI, M.: Intelligent Autopilot Control Design for a 2-DOF Helicopter Model, International Journal of Computers, Communications & Control 3 (2008), 337—342.

[14] KHORRAMABADI, S. S.—BOROUSHAKI, M.—LUCAS, C.: Emotional learning based intelligent controller for a PWR nuclear reactor core during load following operation, Annals of Nuclear Energy 35 No. 2 (2008), 2051–2058.

[15] GHOLIPOUR, A.—LUCAS, C.—SHAHMIRZADI, D.: Predicting Geomagnetic Activity Index by Brain Emotional Learning, WSEAS Transactions on Systems 3 No. 1 (2004), 296–299.

[16] JAFARZADEH, S.—MIRHEIDARI, R.—JAHED, M.—BARK-HORDARI, M.: Designing PID and BELBIC Controllers in Path Tracking Problem”, International Journal of Computers, Communications and Control 3 (2008), 343–348.

Journal of Electrical Engineering

The Journal of Slovak University of Technology

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IMPACT FACTOR 2018: 0.636
5-year IMPACT FACTOR: 0.663

CiteScore 2018: 0.88

SCImago Journal Rank (SJR) 2018: 0.200
Source Normalized Impact per Paper (SNIP) 2018: 0.771

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