Adaptive and robust motion control in presence of LuGre-type friction

Jastrzębski, M.; Kabziński, Jacek; Mosiołek, Przemysław

doi:10.1109/mmar.2017.8046808

2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR) 2017

DOI: 10.1109/mmar.2017.8046808

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Adaptive and robust motion control in presence of LuGre-type friction

M. Jastrzębski

Jacek Kabziński

Przemysław Mosiołek

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2021

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“…Next, the proposed model is a linear-in-parameters one (if the weights of the last layer are considered as parameters) and this type of model is especially attractive for adaptive controller design. This feature of neural models was intensively used in our previous works [15][16][17].…”

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confidence: 99%

Approximation of Permanent Magnet Motor Flux Distribution by Partially Informed Neural Networks

Jastrzębski

Kabziński

2021

Energies

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New results in the area of neural network modeling applied in electric drive automation are presented. Reliable models of permanent magnet motor flux as a function of current and rotor position are particularly useful in control synthesis — allowing one to minimize the losses, analyze motor performance (torque ripples etc.) and to identify motor parameters—and may be used in the control loop to compensate flux and torque variations. The effectiveness of extreme learning machine (ELM) neural networks used for approximation of permanent magnet motor flux distribution is evaluated. Two original network modifications, using preliminary information about the modeled relationship, are introduced. It is demonstrated that the proposed networks preserve all appealing features of a standard ELM (such as the universal approximation property and extremely short learning time), but also decrease the number of parameters and deal with numerical problems typical for ELMs. It is demonstrated that the proposed modified ELMs are suitable for modeling motor flux versus position and current, especially for interior permanent magnet motors. The modeling methodology is presented. It is shown that the proposed approach produces more accurate models and provides greater robustness against learning data noise. The execution times obtained experimentally from well-known DSP boards are short enough to enable application of derived models in modern algorithms of electric drive control.

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confidence: 99%

Approximation of Permanent Magnet Motor Flux Distribution by Partially Informed Neural Networks

Jastrzębski

Kabziński

2021

Energies

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Adaptive and robust motion control in presence of LuGre-type friction

Cited by 1 publication

References 15 publications

Approximation of Permanent Magnet Motor Flux Distribution by Partially Informed Neural Networks

Approximation of Permanent Magnet Motor Flux Distribution by Partially Informed Neural Networks

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