A Machine Learning Based Method to Efficiently Analyze the Cogging Torque Under Manufacturing Tolerances

Reales, Andrea; Jara, Werner; Hermosilla, Gabriel; Madariaga, Carlos; Tapia, Juan A.; Bramerdorfer, Gerd

doi:10.1109/ecce47101.2021.9595571

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Other works base the estimation of cogging torque on artificial intelligence models, neural networks and machine learning [25,26]. Few works, however, combine artificial intelligence models with control algorithms.…”

Section: Cogging Torque Modeling Approachesmentioning

confidence: 99%

“…Figure 17 represents schematically the MPC architecture. Using the plant model parameters and the measurement of x a (k) is evaluated the augmented states over the prediction horizon successively applying the recursion formula, obtaining the set of equations in Equations (25). In matrix form, it is possible to compact as in Equation (26).…”

Section: Model Predictive Controlmentioning

confidence: 99%

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Review on Model Based Design of Advanced Control Algorithms for Cogging Torque Reduction in Power Drive Systems

Dini

Saponara

2022

Energies

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This review of the state of the art aims to collect the description and main research results in the field of development and validation of control algorithms with the main purpose to solve the problem of cogging torque and main sources of electromagnetic torque ripple. In particular, we focus on electric drives for advanced and modern mechatronic applications such as industrial automation, robotics, and automotive applications, with special emphasis on work that exploits model-based design. A great added value of this paper is to explicitly show the operational steps required for the model-based design design of optimized control algorithms for electric drives where it is necessary to make up for electromagnetic torque oscillations due to the main sources of ripple, particularly cogging torque. The ultimate goal of this paper is to provide researchers approaching this particular problem with a comprehensive collection of the most effective solutions reported in the state of the art and also a summary for effectively applying the model-based design methodology.

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Section: Cogging Torque Modeling Approachesmentioning

confidence: 99%

Section: Model Predictive Controlmentioning

confidence: 99%