High performance controller for interior permanent magnet synchronous motor drive using artificial intelligence methods

Hashemi, Zhale; Mardaneh, Mohammad; Sadeghi, Mehdi

doi:10.1016/j.scient.2012.07.001

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…Inspired by biogeography, Simon developed a new approach called Biogeography-Based Optimization (BBO) in 2008. This algorithm is an example of how a natural process can be modeled to solve optimization [14]. In BBO, each possible solution is an island and their features that describe habitability are included in a Habitat Suitability Index (HSI).…”

Section: Biogeography-based Optimization Algorithmmentioning

confidence: 99%

“…In the literature, several speed-control schemes have been proposed for PMSM, where a proper performance in tasks of regulating the speed of the rotor is achieved [7][8][9][10][11][12]. Some controllers use adaptive schemes [8,[13][14][15], artificial neural networks (ANNs) [5,11,12,16], sliding mode control (SMC) based techniques [17][18][19][20], and fuzzy logic [5,10], to name a few methods. Sliding-modes based control provides a high disturbance rejection and a low sensitivity to parametric variations.…”

Section: Introductionmentioning

confidence: 99%

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Parameter Tuning of PI Control for Speed Regulation of a PMSM Using Bio-Inspired Algorithms

et al. 2019

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This article focuses on the optimal gain selection for Proportional Integral (PI) controllers comprising a speed control scheme for the Permanent Magnet Synchronous Motor (PMSM). The gains calculation is performed by means of different algorithms inspired by nature, which allows improvement of the system performance in speed regulation tasks. For the tuning of the control parameters, five optimization algorithms are chosen: Bat Algorithm (BA), Biogeography-Based Optimization (BBO), Cuckoo Search Algorithm (CSA), Flower Pollination Algorithm (FPA) and Sine-Cosine Algorithm (SCA). Finally, for purposes of efficiency assessment, two reference speed profiles are introduced, where an acceptable PMSM performance is attained by using the proposed PI controllers tuned by nature inspired algorithms.

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Section: Biogeography-based Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter Tuning of PI Control for Speed Regulation of a PMSM Using Bio-Inspired Algorithms

et al. 2019

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“…The work can be upgraded to sensor less speed controller by using speed and position estimators. Z. Hashemia [6] An AW controller for an interior permanent magnet synchronous motor drive is presented in this paper. The AW controller parameters have been optimized off-line by using a GA technique with a performance index to reflect the minimum settling time, minimum overshoot/undershoot, and zero steady-state error.…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid Intelligent Controller for Interior Permanent Magnet Synchronous Motor Drives

B¹

2018

IJRASET

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A hybrid neuro-fuzzy scheme for online tuning of a genetic-based proportional-integral (PI) controller for an interior permanent-magnet synchronous motor (IPMSM) drive is presented in this paper. Under various disturbance conditions this proposed controller measures accurate speed control for IPMSM. Based on motor dynamics considering uncertainties initially different operating conditions are obtained in this work. The PI controller parameters in a closed-loop vector control scheme are optimized using genetic algorithm in each operating conditions. The minimum speed deviation, minimum settling time and zero steady-state error is obtained using a performance index in the optimization procedure. The PI controller parameters to ensure optimum drive performance under different disturbances are online tunned using a fuzzy basis function network (FBFN). A natural framework for combining numerical and linguistic information in a uniform fashion are gain by using a proposed FBFN-based PI controller. A digital signal processor board DS 1102 for a laboratory 1-hp IPMSM is used for real time implementation of a proposed controller. Effectiveness of the proposed controller is verified by simulation as well as experimental results at different dynamic operating conditions. A proposed controller is used for an IPMSM drive applications.

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“…is employed. In [29] a high-performance PI controller for an IPMSM drive is developed. The PI is tunned online using the artificial neural network (ANN), this drive is used for MTPA and FW.…”

Section: Introduction mentioning

confidence: 99%

Review of Permanent Magnet Synchronous Motor Fed by Multilevel Inverter

Ahmed¹,

Mohamed²,

Youssef³

et al. 2020

SVU-International Journal of Engineering Sciences and Applicati

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High performance controller for interior permanent magnet synchronous motor drive using artificial intelligence methods

Cited by 9 publications

References 13 publications

Parameter Tuning of PI Control for Speed Regulation of a PMSM Using Bio-Inspired Algorithms

Parameter Tuning of PI Control for Speed Regulation of a PMSM Using Bio-Inspired Algorithms

Hybrid Intelligent Controller for Interior Permanent Magnet Synchronous Motor Drives

Review of Permanent Magnet Synchronous Motor Fed by Multilevel Inverter

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