2019
DOI: 10.1049/iet-epa.2019.0309
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Data‐driven online temperature compensation for robust field‐oriented torque‐controlled induction machines

Abstract: Squirrel-cage induction machines (IMs) with indirect field-oriented control are widely used in industry and are frequently chosen for their accurate and dynamic torque control. During operation, however, temperature rises leading to changes in machine parameters. The rotor resistance, in particular, alters, affecting the accuracy of the torque control. The authors investigated the effect of a rotor resistance parameter mismatch in the control algorithm on the angular rotor flux misalignment and the subsequent … Show more

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Cited by 8 publications
(5 citation statements)
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“…Minimizing the difference between the variational distribution and the exact posterior is thus translated into a cost function comprised of two terms that will be minimized simultaneously. The first term in (10) minimizes the KL-divergence to the prior function, regularizing the solution to obey this prior to some extent. Secondly there is a likelihood cost, which encourages to learn a parameter distribution that maximally explains the training data.…”
Section: B Training a Bnnmentioning
confidence: 99%
See 1 more Smart Citation
“…Minimizing the difference between the variational distribution and the exact posterior is thus translated into a cost function comprised of two terms that will be minimized simultaneously. The first term in (10) minimizes the KL-divergence to the prior function, regularizing the solution to obey this prior to some extent. Secondly there is a likelihood cost, which encourages to learn a parameter distribution that maximally explains the training data.…”
Section: B Training a Bnnmentioning
confidence: 99%
“…Friction is a good example as this phenomenon depends on various environmental factors, is difficult to directly measure and to account for in the physical models [5]- [9]. Other uncertainties are e.g effects of temperature dynamics in electrical actuators that give rise to different torque or force levels [10]. Secondly, the fixed structure of physical modelling makes it hard to C. Van Heck, A. Coene and G. Crevecoeur are with the Department of Electromechanical, Systems and Metal Engineering, Ghent University, B-9052 Ghent, Belgium e-mail: {cevheck.vanheck, annelies.coene, guillaume.crevecoeur}@ugent.be.…”
Section: Introductionmentioning
confidence: 99%
“…Note that the planar motor always executes a repetitive motion task in practical industry application, for instance, laser cutting and micromachining process [17]. Among the various repetitive control methods [18][19][20][21], iterative learning control (ILC) stands out for its excellent ability to capture the repeated information hidden in the previous iteration to improve the closed-loop tracking performance gradually and therefore, it is widely applied in modern precision industry [22]. In Ref.…”
Section: Introductionmentioning
confidence: 99%
“…Thermal prediction of electric motors is becoming increasingly important in the drive for higher power density, energy efficiency and cost reduction [1]. Accurate temperature estimation of motor hotspots is key for condition monitoring and fault detection [2], as well as motor control [3,4]. As the stator windings are surrounded by thermally vulnerable insulation material, real-time prediction of the temperature of the windings allows enhancement of the motor end-of-life duration.…”
Section: Introductionmentioning
confidence: 99%
“…A large portion of the industrial induction motors use Field-Oriented Control (FOC) schemes. They rely on having accurate knowledge of the rotor resistance parameter value for their torque control [4]. Having accurate real-time temperature prediction capabilities thus allows improvement to the torque performance of DTC (i.e., knowledge of the stator windings temperature) and FOC (rotor temperature) as well as enhance end-of-life duration.…”
Section: Introductionmentioning
confidence: 99%