Identification and control of induction motor stator currents using fast on-line random training of a neural network

Burton, B.; Kamran, F.; Harley, R.G.; Habetler, T.G.; Brooke, M.A.; Poddar, R.

doi:10.1109/28.585860

Cited by 57 publications

(20 citation statements)

References 8 publications

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“…Some examples are speed control of dc motors [94], [117], diagnostics of induction motor faults [24], [25], [41], [42], induction motor control [17], [18], [56], [127], and current regulator for pulsewidth-modulation (PWM) rectifiers [31]. Maintenance and sensor failure detection was reported in [82], check valves operating in a nuclear power plant [57], [114], and vibration monitoring in rolling element bearings [2].…”

Section: A Mlpsmentioning

confidence: 99%

A comprehensive review for industrial applicability of artificial neural networks

Meireles

Almeida

Simões

2003

IEEE Trans. Ind. Electron.

382

136

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Abstract-This paper presents a comprehensive review of the industrial applications of artificial neural networks (ANNs), in the last 12 years. Common questions that arise to practitioners and control engineers while deciding how to use NNs for specific industrial tasks are answered. Workable issues regarding implementation details, training and performance evaluation of such algorithms are also discussed, based on a judiciously chronological organization of topologies and training methods effectively used in the past years. The most popular ANN topologies and training methods are listed and briefly discussed, as a reference to the application engineer. Finally, ANN industrial applications are grouped and tabulated by their main functions and what they actually performed on the referenced papers. The authors prepared this paper bearing in mind that an organized and normalized review would be suitable to help industrial managing and operational personnel decide which kind of ANN topology and training method would be adequate for their specific problems.

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Section: A Mlpsmentioning

confidence: 99%

A comprehensive review for industrial applicability of artificial neural networks

Meireles

Almeida

Simões

2003

IEEE Trans. Ind. Electron.

382

136

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“…Previously, it has been shown with simulations that a modified RWC algorithm can identify and control an inductor motor [5]. Further simulation-based research has shown that the RWC algorithm is immune to analog circuit nonidealities [6].…”

Section: A Learning Algorithmmentioning

confidence: 99%

A CMOS feedforward neural-network chip with on-chip parallel learning for oscillation cancellation

Liu

Brooke

Hirotsu

2002

IEEE Trans. Neural Netw.

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Abstract-This paper presents a mixed signal CMOS feedforward neural-network chip with on-chip error-reduction hardware for real-time adaptation. The chip has compact on-chip weighs capable of high-speed parallel learning; the implemented learning algorithm is a genetic random search algorithm-the random weight change (RWC) algorithm. The algorithm does not require a known desired neural-network output for error calculation and is suitable for direct feedback control. With hardware experiments, we demonstrate that the RWC chip, as a direct feedback controller, successfully suppresses unstable oscillations modeling combustion engine instability in real time.Index Terms-Analog finite impulse response (FIR) filter, direct feedback control, neural-network chip, parallel on-chip learning, oscillation cancellation.

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“…The solution presented in this paper is based on the special algorithm, which is used for automatic supervising the calculations, and after a certain number of cycles generates the results in form of the observer parameter set. The process of parameter synthesis was optimised by means on the base of random weight change (RWC) procedure [18] (Fig. 8).…”

Section: Observer Parameters Settingmentioning

confidence: 99%

Position Estimation of the PMSM High Dynamic Drive at Low Speed Range

Urbański¹

2012

MATLAB - A Fundamental Tool for Scientific Computing and Engineering Applications - Volume 1

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Identification and control of induction motor stator currents using fast on-line random training of a neural network

Cited by 57 publications

References 8 publications

A comprehensive review for industrial applicability of artificial neural networks

A comprehensive review for industrial applicability of artificial neural networks

A CMOS feedforward neural-network chip with on-chip parallel learning for oscillation cancellation

Position Estimation of the PMSM High Dynamic Drive at Low Speed Range

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