Parallel Computation of Wrench Model for Commutated Magnetically Levitated Planar Actuator

Abstract

An accurate wrench model is significant for the simulation, manufacture, and control of the commutated magnetically levitated planar actuator (CMLPA). With plenty of coils and permanent magnets employed in the coil set and magnet array of CMLPA, the computational burden of the corresponding wrench model can be substantial. This paper proposes an accurate, universal, and robust parallel massive-thread wrench model (PMWM) for the CMLPA. In PMWM, the magnetic node, Gaussian quadrature, and coordinate transformation are employed to express the interaction between magnet array and coil set. All of these calculation modules are implemented on the graphics processing unit in a massively parallel framework by CUDA. In order to highlight the performance of this PMWM, the wrench model of three different CMLPAs is computed. The computation accuracy and efficiency of proposed PMWM are compared with the finite-element method software Ansys Maxwell and a boundary element method software package named Radia, respectively. The same wrench model is also implemented on a multicore CPU through OpenMP and the comparative results are presented to show significant acceleration of the proposed massive-thread model. Index Terms-Commutated magnetically levitated planar actuator (CMLPA), graphics processing unit (GPU), parallel processing, wrench model.NOMENCLATURE N, M Coils and magnets amount. T Magnetic nodes amount. i, q Coil and coil segment indices. j, k Magnet and magnetic node indices. g 1 , g 2 , g 3 Gaussian quadrature sequence numbers. τ c , τ m Coil pitch and magnet pitch. H m Cuboid or cylindrical magnet height. L m , W m Cuboid magnet length and width. R m Cylindrical magnet radius.

Directed-Driven 8-Phase Magnetically Levitated Rotary Table Based on an Analytical-Numerical Model

“…9. As given in [9], [13], the purpose of the decoupling unit in this system is to solve the exciting current for the coils at each control cycle when the maglev system works. In the implementation, after the desired net force and torque are calculated by the control algorithm, the current values are obtained due to the matrix operation, which is based on the pseudo-inverse matrix of the current-wrench transformation matrix.…”

“…In the past few decades, magnetically levitated tables with different topology structures [9], [10] have been proposed. In [10], [11], the maglev positioning tables are developed based on several Lorentz force actuators.…”

Extended unified wrench model suitable for the end effect of the ironless permanent magnet planar motor

“…Nevertheless, the special ratio bring a certain restriction to design. De Boeij et al [7], Berkelman and Dzadovsky [24], Miyasaka and Berkelman [25] used the lookup table method and Xu et al [26] utilised the parallel computation to conduct the modelling of the IPMPM including end effect. Compared with the earlier discussed models with analytical expression, these methods have relative lower modelling efficiency which is inconvenient for the analysis and optimisation of the IPMPM.…”

Utilización de GPU-CUDA en el Procesamiento Digital de Imágenes

“…Entre las plataformas paralelas existentes destaca la Arquitectura Unificada de Dispositivos de Cómputo (CUDA) (Saxena, Sharma and Sharma, 2014), (Vokorokos et al, 2014), la cual permite acelerar aplicaciones de cómputo, aprovechando el poder de las GPU´s (NVIDIA, no date). Recientemente se ha incrementado el uso de la plataforma CUDA (Pawar, 2017), Sin embargo, solo se detallan revisiones del estado del arte llevadas a cabo para acelerar la velocidad de procesamiento digital de imágenes (PDI) en ciertas aplicaciones como la médica (Weinlich et al, 2013) (Jiansen Li et al, 2014) (Lee et al, 2013) y no cubre las diferentes aplicaciones de procesamiento de video utilizando GPU en video vigilancia (Jha and Trivedi, 2013), (Devani, Nikam and Meshram, 2015), (Deligiannidis and Arabnia, 2014), procesamiento de imágenes de Radar de Apertura Sintética (SAR) (Fatica and Phillips, 2014), mejora de súper resolución de imágenes (Feng, Zhang and Gao, 2015), reconocimiento de objetos utilizando descriptor de Fourier (Haythem et al, 2014), criptografía, seguimiento de objetos, reducción de ruido (Yazdanpanah et al, 2014), reconstrucción de imágenes (Zhu et al, 2013) (Kau and Chen, 2013) (Heidari, 2013), detección de rostros (Sun et al, 2013), modelos de actuadores planares (Xu, Dinavahi and Xu, 2016), etc.…”