On-line adaptive artificial neural network based vector control of permanent magnet synchronous motors

Rahman, M.A.; Hoque, Md. Ashraful

doi:10.1109/60.736315

Cited by 165 publications

(51 citation statements)

References 15 publications

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“…But in nonlinear system identification without expert supervisor will not have a high capability. On the contrary of this statement can be seen in neural networks [15,16]. For use of capabilities of these two approaches, fuzzy neural systems are formed [17][18][19].…”

Section: Fuzzy Neural Systemsmentioning

confidence: 94%

Torque Control of AC Motor with FOPID Controller Based on Fuzzy Neural Algorithm

Farahani¹

2017

IJICS

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Section: Fuzzy Neural Systemsmentioning

confidence: 94%

Torque Control of AC Motor with FOPID Controller Based on Fuzzy Neural Algorithm

Farahani¹

2017

IJICS

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“…In this paper, the state equation of the PM SM is developed based on the dq-axis model in rotor reference frame and the stator currents can be measured. The whole control system is implemented in M atlab/Simulink platform as shown in Fig The state equation of the unsaturated model of a PMSM can be expressed as (1). Although the parameters in (1) will be varying nonlinearly when the magnet is saturated or the temperature rises, the proposed estimators will work well in tracking the varying parameters.…”

Section: Vector Control System and Design Parametersmentioning

confidence: 99%

“…Different NN strategies have different tracking algorithms and the convergence speeds of different NNs are controllable. In [1], an adaptive NN speed estimator was introduced, while a strategy which uses two NNs was developed in [2] to cooperatively estimate the parameters. However, the stabilities of these NN algorithms are not considered and the robustness cannot be confirmed due to the mismatching of some unidentified and temperature-variation p arameters.…”

Section: (2) Based On Kalman Filters: Kalman Filter Is One Important mentioning

confidence: 99%

Comparison of two novel MRAS based strategies for identifying parameters in permanent magnet synchronous motors

Liu

Zhang

Zhu

et al. 2010

Int. J. Autom. Comput.

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It shows that the two estimators are simple and have good robustness against parameter variation and are accurate in parameter tracking. However, the estimator based on Popov stability criterion, which can overcome the parameter variation in a practical system, is superior in terms of response speed and convergence speed since there are both proportional and integral units in the estimator in contrast to only one integral unit in the estimator based on Lyapunov stability theorem. In addition, there is no need of the expert experience which is required in designing a Lyapunov function.

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“…However, the control performance of the PM motors is greatly affected by the uncertainties related to the motor parameters, external load disturbance, and unmodeled and nonlinear dynamics [3]. Advanced control techniques such as nonlinear control [4], adaptive control [5], robust control [6], variable structure control, and intelligent control have been developed. However, the overall stability may not be guaranteed in these schemes due to certain assumptions introduced, complicated controller design, and feedback linearization.…”

mentioning

confidence: 99%

A General Magnetic-Energy-Based Torque Estimator: Validation via a Permanent-Magnet Motor Drive

Cossar

Popescu

Miller

et al. 2008

IEEE Trans. on Ind. Applicat.

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Abstract-This paper describes the use of the currentflux-linkage (i−ψ) diagram to validate the performance of a general magnetic-energy-based torque estimator. An early step in the torque estimation is the use of controller duty cycles to reconstruct the average phase-voltage waveform during each pulsewidthmodulation (PWM) switching period. Samples over the fundamental period are recorded for the estimation of the average torque. The fundamental period may not be an exact multiple of the sample time. For low speed, the reconstructed voltage requires additional compensation for inverter-device losses. Experimental validation of this reconstructed waveform with the actual PWM phase-voltage waveform is impossible due to the fact that one is PWM in nature and the other is the average value during the PWM period. A solution to this is to determine the phase flux-linkage using each waveform and then plot the resultant i−ψ loops. The torque estimation is based on instantaneous measurements and can therefore be applied to any electrical machine. This paper includes test results for a three-phase interior permanent-magnet brushless ac motor operating with both sinusoidal and nonsinusoidal current waveforms.Index Terms-Brushless permanent-magnet (PM) motor drives, electromagnetic average torque, torque estimator.

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On-line adaptive artificial neural network based vector control of permanent magnet synchronous motors

Cited by 165 publications

References 15 publications

Torque Control of AC Motor with FOPID Controller Based on Fuzzy Neural Algorithm

Torque Control of AC Motor with FOPID Controller Based on Fuzzy Neural Algorithm

Comparison of two novel MRAS based strategies for identifying parameters in permanent magnet synchronous motors

A General Magnetic-Energy-Based Torque Estimator: Validation via a Permanent-Magnet Motor Drive

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