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/ias.1995.530522

Cited by 29 publications

(8 citation statements)

References 10 publications

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“…Therefore, the performance of such an off-line trained NN controller depends upon the amount and quality of training data used and is also sensitive to parameter variations. For systems where parameters variations have to be compensated, an on-line trained NN controller can be applied [111], [112], [116]. In [112], an NN induction motor CC with parameter identification was proposed.…”

Section: ) Nn's Controllersmentioning

confidence: 99%

“…For systems where parameters variations have to be compensated, an on-line trained NN controller can be applied [111], [112], [116]. In [112], an NN induction motor CC with parameter identification was proposed. To achieve very fast on-line training (8 s for one training cycle) a new algorithm called random weight change (RWC) is applied.…”

Section: ) Nn's Controllersmentioning

confidence: 99%

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Current control techniques for three-phase voltage-source PWM converters: a survey

Kaźmierkowski

Malesani

1998

IEEE Trans. Ind. Electron.

1,810

671

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The aim of this paper is to present a review of recently used current control techniques for three-phase voltagesource pulsewidth modulated converters. Various techniques, different in concept, have been described in two main groups: linear and nonlinear. The first includes proportional integral stationary and synchronous) and state feedback controllers, and predictive techniques with constant switching frequency. The second comprises bang-bang (hysteresis, delta modulation) controllers and predictive controllers with on-line optimization. New trends in the current control-neural networks and fuzzy-logicbased controllers-are discussed, as well. Selected oscillograms accompany the presentation in order to illustrate properties of the described controller groups.

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Section: ) Nn's Controllersmentioning

confidence: 99%

Section: ) Nn's Controllersmentioning

confidence: 99%

Current control techniques for three-phase voltage-source PWM converters: a survey

Kaźmierkowski

Malesani

1998

IEEE Trans. Ind. Electron.

1,810

671

View full text Add to dashboard Cite

show abstract

“…Due to the non-linear character of the controller, the RWC procedure is selected (Burton et al, 1997). This procedure is fast and insensitive to the local minimum of the optimised criterion.…”

Section: Neural Controller Synthesismentioning

confidence: 99%

“…The first stage consists in training the ANN to form the proper shape of the control surface, which represents the non-linear characteristic of the controller. At the second stage, the PI controller settings are adjusted by means of the random weight change (RWC) procedure (Burton et al, 1997) which optimises the control quality index formulated in the paper. The synthesis is performed using simulation techniques and subsequently the behaviour of a laboratory speed control system is validated in the experimental set-up.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network based robust speed control of permanent magnet synchronous motors

Pajchrowski

Urbański

Zawirski

2006

COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

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PurposeThe aim of the paper is to find a simple structure of speed controller robust against drive parameters variations. Application of artificial neural network (ANN) in the controller of PI type creates proper non‐linear characteristics, which ensures controller robustness.Design/methodology/approachThe robustness of the controller is based on its non‐linear characteristic introduced by ANN. The paper proposes a novel approach to neural controller synthesis to be performed in two stages. The first stage consists in training the ANN to form the proper shape of the control surface, which represents the non‐linear characteristic of the controller. At the second stage, the PI controller settings are adjusted by means of the random weight change (RWC) procedure, which optimises the control quality index formulated in the paper. The synthesis is performed using simulation techniques and subsequently the behaviour of a laboratory speed control system is validated in the experimental set‐up. The control algorithms of the system are performed by a microprocessor floating point DSP control system.FindingsThe proposed controller structure with proper control surface created by ANN guarantees expected robustness.Originality/valueThe original method of robust controller synthesis was proposed and validated by simulation and experimental investigations.

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“…The use of ANNs for the control of motor drives has become very popular during the last ten years. This can be verified from references [4,9]. In all cases, training of the neural network is the most important function.…”

Section: Introductionmentioning

confidence: 89%

Parallel computation of continually on-line trained neural networks for identification and control of induction motors

Rubaai

Kotaru²,

Kankam³

Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370)

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This paper presents an adaptive parallel processing control scheme, using an artificial neural network (ANN) which is trained while the controller is operating on-line. The proposed control structure incorporates five-multilayer feedforward ANNs, which are on-line, trained, using the Levenburg-Marquadt learning method. The five networks are used exclusively for system estimation. The estimation mechanism uses continual on-line training to learn the unknown stator model dynamics and estimate the rotor fluxes of an inverter-fed induction motor. Subsequently, the estimated stator currents are fed into an adaptive controller to track the desired stator current trajectories. The adaptive controller is constructed as a feedback signal (a predetermined control law), depending on estimated stator currents supplied by the neural estimators and the reference trajectories to be tracked by the output. The control of the direct and quadrature components of the stator current successfully tracked a wide variety of reference trajectories after relatively short, on-line training periods. This paper also suggests two three-layer ANNs control scheme to simultaneously identify and adaptively adjust the rotor speed to follow a predetermined reference track.

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

Cited by 29 publications

References 10 publications

Current control techniques for three-phase voltage-source PWM converters: a survey

Current control techniques for three-phase voltage-source PWM converters: a survey

Artificial neural network based robust speed control of permanent magnet synchronous motors

Parallel computation of continually on-line trained neural networks for identification and control of induction motors

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