Precision motion control of permanent magnet linear motors

Zhang, Dailin; Chen, Youping; Ai, Wu; Zhou, Zude

doi:10.1007/s00170-006-0727-8

Cited by 30 publications

(17 citation statements)

References 15 publications

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“…w 1i and w 2i (i = 1, 2, 3, … , n) are the weights of the ith neural cell from the input layer to the hidden layer and from the hidden layer to the output layer, respectively. In theory, a BP neural network can approximate any curve, and the noise in the curve can be reduced effectively [21].…”

Section: Prediction Model Of the Tracking Errormentioning

confidence: 99%

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Iterative pre-compensation scheme of tracking error for contouring error reduction

Zhang

Chen

2016

Int J Adv Manuf Technol

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Contouring errors calculated from the tracking errors of the individual servo drives is one of the important factors that affect machining accuracy. Through the precompensation of the tracking errors the contouring error can be reduced. This paper builds the prediction model of the tracking error, based on which iterative pre-compensation scheme is proposed, to achieve the optimum precompensation value without running servo drives repetitively. Simulation and experiment results verify that the tracking error can be predicted accurately by the prediction model and the iterative pre-compensation scheme of the tracking error can reduce the contouring error effectively.

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Section: Prediction Model Of the Tracking Errormentioning

confidence: 99%

“…[21], the big curvature segments of a curve are difficult to be fully pre-compensated by the direct precompensation method. And the disturbance error composed of small noises and some big curvature spikes is also difficult to be fully pre-compensated.…”

Section: Iterative Pre-compensation Of the Tracking Errormentioning

confidence: 99%

Iterative pre-compensation scheme of tracking error for contouring error reduction

Zhang

Chen

2016

Int J Adv Manuf Technol

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“…This energy will be used to actuate the mechanism, example ejector mechanism. Others, Zhang, Chen, Ai, and Zhou (2007) and Yan and Cheng (2009) discussed artificial tools namely BP and neural. Both have a major drawback: slow convergence speed and problem of local minima.…”

Section: Introductionmentioning

confidence: 99%

Experimental PC based TGPID control method for 2D CNC machine

Nurhadi

Tarng

2011

Expert Systems with Applications

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“…In , the force ripple can be modeled as a disturbance in the position control system, a disturbance observer is used as a feedback compensator to compensate the force disturbances, but when PMSLM run at low speed, because of the inaccurately detected velocity and acceleration, the force disturbances can not be effectively suppressed. In , based on a disturbance observer, a feedforward neural network is used to approximate and further compensate the force ripple, but its learning cost is considerably large. Since the presence of the force ripple is periodic with the linear position of PMSLM , the identification of the ripple model parameters is an effective solution for suppressing the force ripple.…”

Section: Introductionmentioning

confidence: 99%

Identification and Compensation of Force Ripple in PMSLM using a JITL Technique

Lü¹,

Tang²,

Song³

et al. 2014

Asian Journal of Control

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This paper presents an adaptive compensation scheme for reducing the force ripple effects in permanent magnet synchronous linear motor (PMSLM). Firstly, when the velocity of a linear motor is close to zero, based on a fast Fourier transform (FFT) analysis, the dominant displacement periodicity of the force ripple can be obtained for simplifying the ripple model. Next, to enhance the identification accuracy and guarantee the real-time performance of the linear servo system simultaneously, an improved just-in-time learning (JITL) algorithm is proposed to estimate the ripple model parameters. Finally, based on the identification results, an adaptive feedforward controller is implemented in the linear servo system to compensate the force ripple. The main motivation of this paper is the extension of the identification methods to improve the servo control performance in industrial applications. Simulation and experimental results demonstrate the effectiveness of the proposed method, even when compared to a traditional compensation method based on a recursive least squares algorithm.

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Precision motion control of permanent magnet linear motors

Cited by 30 publications

References 15 publications

Iterative pre-compensation scheme of tracking error for contouring error reduction

Iterative pre-compensation scheme of tracking error for contouring error reduction

Experimental PC based TGPID control method for 2D CNC machine

Identification and Compensation of Force Ripple in PMSLM using a JITL Technique

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