Optimal Load Angle Learning Algorithm for Sensorless Closed-Loop Stepping Motor Control

Viaene, Jasper De; Ceulemans, David; Derammelaere, Stijn; Stockman, Kurt

doi:10.1109/iemdc.2019.8785208

Cited by 3 publications

(5 citation statements)

References 10 publications

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“…To overcome this hurdle, [16] From equation (1), it was previously deduced that the motor is the most energy-efficient when the load angle δ equals π/2 rad. However, [24] elucidated that this optimum is an unstable operating point, and to guarantee stable operation, the load angle setpoint δ * should preferably be chosen lower. Based on [24], it can be concluded that a reduced estimator lag results in a smaller safety margin to the theoretical optimum, and eventually, a more energy-efficient drive.…”

Section: A Torque Generationmentioning

confidence: 99%

“…However, [24] elucidated that this optimum is an unstable operating point, and to guarantee stable operation, the load angle setpoint δ * should preferably be chosen lower. Based on [24], it can be concluded that a reduced estimator lag results in a smaller safety margin to the theoretical optimum, and eventually, a more energy-efficient drive. To reduce the estimator lag, the Fourier transform is considered in more detail.…”

Section: A Torque Generationmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Load Angle Estimation Through an Angular Domain GDFT Filter for Optimal Energetic BLDC Control

Ceulemans

Viaene

Steckel

et al. 2022

IEEE/ASME Trans. Mechatron.

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These days, BLDC motors are increasingly used to drive speed-controlled applications (e.g., drones, air compressors, etc.) thanks to their high power density, good torque inertia ratio, and their simple square wave commutation principle. However, with growing attention towards energy usage, a more efficient sinusoidal current wave is favored over the classical square wave profile. Nevertheless, changing the waveform to absent the silence phase implicates that simple control principles (e.g., hallsensors or back-EMF tracing) are no longer functional, and a more advanced sensorless solution is required. Earlier, an innovative sensorless load-angle-based controller was suggested with the potential to overcome all restrictions concerning stateof-the-art sensorless control. One of the proposed estimator's fundamental building blocks is a sliding discrete Fourier filter (SDFT), requiring both a signal with a fixed base frequency and a full signal period of samples before updating completely. Both requirements severely limit the application field. This paper presents an enhanced load angle estimator that can estimate the load angle 66% faster on average and without quality deterioration, regardless of the rotor speed. Both speed and accuracy of the estimated load angle are validated based on BLDC-measurements, confirming the potential and revealing possible further estimation lag decreases if a limited estimation quality regression is allowed.

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Section: A Torque Generationmentioning

confidence: 99%

Section: A Torque Generationmentioning

confidence: 99%

Dynamic Load Angle Estimation Through an Angular Domain GDFT Filter for Optimal Energetic BLDC Control

Ceulemans

Viaene

Steckel

et al. 2022

IEEE/ASME Trans. Mechatron.

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show abstract

“…In De Viaene et al (2018b), the latter problem is tackled by defining a sensing procedure to determine the settings of an adaptive proportional-integral (PI)-controller with the settling time of the current reduction as design constraint. In addition, the optimal load angle highly depends on the operating point of the stepping motor (De Viaene et al, 2019). Therefore, a logically setting of the load angle setpoint is not that obvious.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, a logically setting of the load angle setpoint is not that obvious. This problem is partially solved in De Viaene et al (2019) by exploring the limits of the stability motor operating range which highly increase the risk on step loss.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the assessment of these parameters are difficult as their values vary with the motor operating conditions (Bertoldi and Atanasiu, 2009). In practice, the estimator and control techniques in De Viaene et al (2018a), Derammelaere et al (2017), De Viaene et al (2018b), and De Viaene et al (2019) are not directly implementable. Therefore, in this paper, it is preferred to shift the focus from the load angle to the cos ( ϕ ) .…”

Section: Introductionmentioning

confidence: 99%

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Self-learning current optimizing control for conventional stepping motor drive technique based on step pulses

Viaene

Ceulemans

Derammelaere

et al. 2021

Transactions of the Institute of Measurement and Control

Self Cite

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The essential advantage of the conventional stepping motor drive technique bases on step command pulses is the ability of open-loop positioning. By ruling out the cost of a position sensor, stepping motors are preferred in low power positioning applications. However, machine developers also want to obtain high dynamics with these small and cheap stepping motors. For that reason, stepping motors are used at its limits as much as possible. A drawback of the open-loop control is the continuous risk of missing a step due to overload. Due to this uncertainty, robustness is a major issue in stepping motor applications. Until today, to reduce the possibility of step loss, the motor is typically driven at maximum current level or is over-dimensioned with results in low-efficiency. Therefore in this paper, a self-learning [Formula: see text]-controller optimizing the current is presented. Moreover, to allow broad industrial applicability, this technique is computationally simple, needs no mechanical or electrical parameter knowledge and take into account the unique character of stepping motors and their conventional drive technique based on step command pulses. The proposed algorithm is validated through measurements on a hybrid stepping motor.

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Time-optimal stepper motor motion profile through a novel load-angle-based step-command optimization

Ceulemans

Oosterwyck

Demetsenaere

et al. 2021

2021 IEEE International Electric Machines &Amp; Drives Conference (IEMDC)

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Optimal Load Angle Learning Algorithm for Sensorless Closed-Loop Stepping Motor Control

Cited by 3 publications

References 10 publications

Dynamic Load Angle Estimation Through an Angular Domain GDFT Filter for Optimal Energetic BLDC Control

Dynamic Load Angle Estimation Through an Angular Domain GDFT Filter for Optimal Energetic BLDC Control

Self-learning current optimizing control for conventional stepping motor drive technique based on step pulses

Time-optimal stepper motor motion profile through a novel load-angle-based step-command optimization

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