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An Online Observer for Minimization of Pulsating Torque in SMPM Motors.

Roșca L, Duguleană M - PLoS ONE (2016)

Bottom Line: Either the motor design or the motor control needs to be improved in order to minimize the periodic disturbances.The compensating signal is identified and added as feedback to the control signal of the servo motor.Compensation is evaluated for different values of the input signal, to show robustness of the proposed method.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Engineering, University "Lucian Blaga" of Sibiu, Sibiu, Romania.

ABSTRACT
A persistent problem of surface mounted permanent magnet (SMPM) motors is the non-uniformity of the developed torque. Either the motor design or the motor control needs to be improved in order to minimize the periodic disturbances. This paper proposes a new control technique for reducing periodic disturbances in permanent magnet (PM) electro-mechanical actuators, by advancing a new observer/estimator paradigm. A recursive estimation algorithm is implemented for online control. The compensating signal is identified and added as feedback to the control signal of the servo motor. Compensation is evaluated for different values of the input signal, to show robustness of the proposed method.

No MeSH data available.


Principle of model parameters estimation.
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pone.0153255.g011: Principle of model parameters estimation.

Mentions: The block diagram of the principle behind the implementation of parameter estimation module is presented in Fig 11. The difference between the periodical disturbances model output and the filtered signal output y(t) represents the observation error e(t). Eq 18 represents the general form of a recursive estimation algorithm [46]:Θ^(t)= Θ^(t−1)+K(t)(y(t)−y^(t))(18)where is a vector of two values, amplitude and phase, y(t) is the filter output, is the prediction of y(t) based on observations up to time (t-1), and K(t) is:K(t)=Q(t)ψ(t)(19)


An Online Observer for Minimization of Pulsating Torque in SMPM Motors.

Roșca L, Duguleană M - PLoS ONE (2016)

Principle of model parameters estimation.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835102&req=5

pone.0153255.g011: Principle of model parameters estimation.
Mentions: The block diagram of the principle behind the implementation of parameter estimation module is presented in Fig 11. The difference between the periodical disturbances model output and the filtered signal output y(t) represents the observation error e(t). Eq 18 represents the general form of a recursive estimation algorithm [46]:Θ^(t)= Θ^(t−1)+K(t)(y(t)−y^(t))(18)where is a vector of two values, amplitude and phase, y(t) is the filter output, is the prediction of y(t) based on observations up to time (t-1), and K(t) is:K(t)=Q(t)ψ(t)(19)

Bottom Line: Either the motor design or the motor control needs to be improved in order to minimize the periodic disturbances.The compensating signal is identified and added as feedback to the control signal of the servo motor.Compensation is evaluated for different values of the input signal, to show robustness of the proposed method.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Engineering, University "Lucian Blaga" of Sibiu, Sibiu, Romania.

ABSTRACT
A persistent problem of surface mounted permanent magnet (SMPM) motors is the non-uniformity of the developed torque. Either the motor design or the motor control needs to be improved in order to minimize the periodic disturbances. This paper proposes a new control technique for reducing periodic disturbances in permanent magnet (PM) electro-mechanical actuators, by advancing a new observer/estimator paradigm. A recursive estimation algorithm is implemented for online control. The compensating signal is identified and added as feedback to the control signal of the servo motor. Compensation is evaluated for different values of the input signal, to show robustness of the proposed method.

No MeSH data available.