Online Tracking of Varying Inertia using a SDFT Approach

Vanbecelaere, Foeke; Derammelaere, Stijn; Nevaranta, Niko; Viaene, Jasper De; Verbelen, Florian; Stockman, Kurt; Monte, Michael

doi:10.1016/j.mechatronics.2020.102361

Cited by 11 publications

(1 citation statement)

References 22 publications

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“…The load is modelled using lumped parameters: inertia J, viscous damping b and a load torque T load . This simplification is permissible as higher-order mechanical systems will often show behaviour that is very similar to that of the second-order systems as in ( 7) [25].…”

Section: A System Dynamicsmentioning

confidence: 99%

Sensorless Load Angle Control for Energy Optimal Sinusoidal Driven BLDC Motor Applications

Deviaene

Ceulemans

Derammelaere

et al. 2022

IEEE/ASME Trans. Mechatron.

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Section: A System Dynamicsmentioning

confidence: 99%

Sensorless Load Angle Control for Energy Optimal Sinusoidal Driven BLDC Motor Applications

Deviaene

Ceulemans

Derammelaere

et al. 2022

IEEE/ASME Trans. Mechatron.

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Online tracking of dynamically time-varying inertia using an enhanced SDFT-based estimation methodology

Ceulemans,

Vanbecelaere,

Van Oosterwyck

et al. 2024

Discov Mechanical Engineering

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The overall motion performance of mechatronic machines depends heavily on proper knowledge of the machine’s characteristics, among which the inertia. Moreover, the inertia of most multi-body mechanisms, for example, reciprocating slider-crank mechanisms, varies as a function of the machine position, thereby challenging optimal control. Nevertheless, the inertia’s variation is sometimes not known accurately enough or changes over time due to, e.g. a production process or premature wear, forcing an online control/correction of the inertia profile. Therefore, earlier, the author developed a new computational-friendly online method based on a frequency-specific magnitude response to estimate the machine’s position-dependent load side inertia, assuming good knowledge of all other machine properties. Yet, to guarantee accuracy, the maximum machine speed during estimation had to be strongly reduced, resulting in an undesirable longer motion time. Therefore, in this work, the failure mechanism of the earlier proposed estimator is analysed and overcome by intelligently separating estimation and motion signals. As a result, the estimator is no longer limited by the machine’s motion speed but rather by a maximum in the inertia variation per signal excitation period and, thus, time. Moreover, given this limiting variation, a guideline was established to evaluate the new method’s estimation quality. Finally, both in simulation and on a physical machine, by taking advantage of the newly proposed method, the minimal required estimation time is reduced significantly (up to 50% on the machine) without diminishing the estimation accuracy.

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Soft switching multiple model predictive control with overlapping cross-over time strategy in an industrial high speed pick and place application

Mehdizadeh Gavgani,

Farnam,

Vanbecelaere

et al. 2024

Control Engineering Practice

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Online Tracking of Varying Inertia using a SDFT Approach

Cited by 11 publications

References 22 publications

Sensorless Load Angle Control for Energy Optimal Sinusoidal Driven BLDC Motor Applications

Sensorless Load Angle Control for Energy Optimal Sinusoidal Driven BLDC Motor Applications

Online tracking of dynamically time-varying inertia using an enhanced SDFT-based estimation methodology

Soft switching multiple model predictive control with overlapping cross-over time strategy in an industrial high speed pick and place application

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