Neural Network-Based Model Reference Adaptive System for Torque Ripple Reduction in Sensorless Poly Phase Induction Motor Drive

This paper proposes the modified, extended Kalman filter, neural network-based model reference adaptive system and the modified observer technique to estimate the speed of a five-phase induction motor for sensorless drive. The proposed method is generated to achieve reduced speed deviation and reduced torque ripple efficiently. In inclusion, the result of speed performance and torque ripple under parameter variations were analysed and compared with the conventional direct synthesis method. The speed estimation of a five-phase motor in the four methods is analysed using MATLAB Simulink platform, and the optimum method is recognized using time domain analysis. It is observed that speed error is minimized by 60% and torque ripple is reduced by 75% in the proposed method. The hardware setup is carried out for the optimized method identified.

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A neural network model reference adaptive system for speed estimation of sensorless induction motor

2017 29th Chinese Control And Decision Conference (CCDC) ◽

10.1109/ccdc.2017.7978416 ◽

2017 ◽

Cited By ~ 1

Author(s):

Youlin Yang

Keyword(s):

Neural Network ◽

Induction Motor ◽

Network Model ◽

Neural Network Model ◽

Adaptive System ◽

Speed Estimation ◽

Model Reference ◽

Model Reference Adaptive System ◽

Model Reference Adaptive

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Speed sensorless of a bearingless induction motor based on super-twisting-model reference adaptive system

2017 20th International Conference on Electrical Machines and Systems (ICEMS) ◽

10.1109/icems.2017.8056236 ◽

2017 ◽

Author(s):

Weiming Sun ◽

Zebin Yang ◽

Xiaodong Sun ◽

Dan Zhang

Keyword(s):

Induction Motor ◽

Adaptive System ◽

Speed Sensorless ◽

Model Reference ◽

Model Reference Adaptive System ◽

Model Reference Adaptive

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Comparison of sensorless techniques based on Model Reference Adaptive System for induction motor drives

2015 IEEE 13th Brazilian Power Electronics Conference and 1st Southern Power Electronics Conference (COBEP/SPEC) ◽

10.1109/cobep.2015.7420106 ◽

2015 ◽

Author(s):

Diego D. Pinheiro ◽

Carlos M. O. Stein ◽

Jean P. Costa ◽

Rafael Cardoso ◽

Emerson G. Carati

Keyword(s):

Induction Motor ◽

Adaptive System ◽

Motor Drives ◽

Induction Motor Drives ◽

Model Reference ◽

Model Reference Adaptive System ◽

Model Reference Adaptive

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Latest Advances in DTC Control of Induction Motors

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f1008.0386s20 ◽

2020 ◽

Vol 8 (6S) ◽

pp. 120-123

Keyword(s):

Induction Motor ◽

Predictive Control ◽

Induction Motors ◽

Adaptive System ◽

Sliding Mode ◽

Direct Torque Control ◽

Torque Ripple ◽

Torque Control ◽

Model Reference Adaptive System ◽

Model Reference Adaptive

In these days, developments in the area of Induction Motor control is increasing significantly. Considerable advancements have been taken place in the area of Direct Torque Control (DTC), which is capable of providing quick dynamic response with respect to torque and flux. This paper presents a detailed survey on various latest techniques of DTC control of Induction Motor such as DTC-SVM with hysteresis band, DTCSVM with Model Predictive Control, DTC with sliding mode control, DTC with Model reference adaptive system (MRAS) et cetera. The simulation results are discussed for DTC-SVPWM topology and results obtained proves that this method has reduced torque ripple

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Model reference adaptive system pseudoreduced-order flux observer for very low speed and zero speed estimation in sensorless induction motor drives

2002 IEEE 33rd Annual IEEE Power Electronics Specialists Conference. Proceedings (Cat. No.02CH37289) ◽

10.1109/psec.2002.1023886 ◽

2003 ◽

Cited By ~ 12

Author(s):

H. Madadi Kojabadi ◽

L. Chang

Keyword(s):

Induction Motor ◽

Adaptive System ◽

Motor Drives ◽

Speed Estimation ◽

Induction Motor Drives ◽

Model Reference ◽

Low Speed ◽

Flux Observer ◽

Model Reference Adaptive System ◽

Model Reference Adaptive

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Simulation and experimental studies of model reference adaptive system for sensorless induction motor drive

Simulation Modelling Practice and Theory ◽

10.1016/j.simpat.2005.01.002 ◽

2005 ◽

Vol 13 (6) ◽

pp. 451-464 ◽

Cited By ~ 33

Author(s):

H. Madadi Kojabadi

Keyword(s):

Induction Motor ◽

Adaptive System ◽

Experimental Studies ◽

Motor Drive ◽

Induction Motor Drive ◽

Model Reference ◽

Model Reference Adaptive System ◽

Model Reference Adaptive

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Performance of a Modified Stator Current Based Model Reference Adaptive System Observer in Sensorless Induction Motor Drives

Electric Power Components and Systems ◽

10.1080/15325008.2018.1527866 ◽

2018 ◽

Vol 46 (16-17) ◽

pp. 1857-1871 ◽

Cited By ~ 1

Author(s):

Mohamed S. Zaky ◽

Mohamed K. Metwaly ◽

Haitham Z. Azazi ◽

Said A. Deraz

Keyword(s):

Induction Motor ◽

Adaptive System ◽

Motor Drives ◽

Induction Motor Drives ◽

Model Reference ◽

Stator Current ◽

Model Reference Adaptive System ◽

Model Reference Adaptive

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Simultaneous speed and rotor time constant identification of an induction motor drive based on the model reference adaptive system combined with a fuzzy resistance estimator

1998 International Conference on Power Electronic Drives and Energy Systems for Industrial Growth, 1998. Proceedings. ◽

10.1109/pedes.1998.1330693 ◽

2005 ◽

Cited By ~ 3

Author(s):

J. Soltani ◽

B. Mizaeian

Keyword(s):

Induction Motor ◽

Time Constant ◽

Adaptive System ◽

Motor Drive ◽

Induction Motor Drive ◽

Model Reference ◽

Model Reference Adaptive System ◽

Model Reference Adaptive

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Secondary resistance identification of an induction motor applied model reference adaptive system and its characteristics.

IEEJ Transactions on Power and Energy ◽

10.1541/ieejpes1972.106.97 ◽

1986 ◽

Vol 106 (2) ◽

pp. 97-104 ◽

Cited By ~ 1

Author(s):

Hidehiko Sugimoto ◽

Shinzo Tamai

Keyword(s):

Induction Motor ◽

Adaptive System ◽

Model Reference ◽

Secondary Resistance ◽

Applied Model ◽

Model Reference Adaptive System ◽

Model Reference Adaptive

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Sensorless Induction Motor Drive Based on Model Reference Adaptive System Scheme Utilising a Fictitious Resistance

Power Electronics and Drives ◽

10.2478/pead-2020-0015 ◽

2020 ◽

Vol 5 (1) ◽

pp. 199-213

Author(s):

Soufien Hajji ◽

Ramzi Ben Chehida ◽

Hichem Zayani ◽

Noomen Bouaziz ◽

Youssef Agrebi Zorgani

Keyword(s):

Induction Motor ◽

Adaptive System ◽

Reference Model ◽

Induction Machine ◽

Motor Drive ◽

Induction Motor Drive ◽

Adaptation Mechanism ◽

Model Reference ◽

Model Reference Adaptive System ◽

Model Reference Adaptive

AbstractThis article presents a new development of an indirect stator flux-oriented controller for sensorless speed induction motor drive utilising instantaneous and steady-state values, respectively, of a fictitious resistance symbolised as R_f. The dimension of the fictitious quantity, in this context, is the ohm, which is the difference between the stator d- and q-axis fictitious resistances. However, from the measurement of the stator voltage and currents of the machine, two independent resistance estimators are built. Therefore, the first is considered as a reference model of the induction machine (IM), and the second is considered as an adjustable model. Subsequently, the error between the states of the two models is used to drive a suitable adaptation mechanism that generates the estimation of the speed, for the adjustable model. Furthermore, the structure of the proposed estimator is free from stator resistance and eliminates the requirement of any flux computation. All the detailed simulation study is carried out in MATLAB/Simulink to validate the proposed method and to highlight the robustness and the stability of the proposed model reference adaptive system estimator.

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