Backlash identification for PMSM servo system based on relay feedback
Backlash identification for PMSM servo system based on relay feedback
复制标题
基于继电器反馈的PMSM伺服系统齿隙识别
DOI：
10.1007/s11071-016-2650-3
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发表时间：
2016-02
影响因子：
5.6
通讯作者：
Wu Jianhua
中科院分区：
文献类型：
--
作者：
Han Yong;Liu Chao;Wu Jianhua
提出了一种基于继电反馈技术的永磁同步电机伺服系统齿隙辨识新方法。我们开发了这种方法，通过分析电机速度信号在时域下的一个强有力的假设下，速度信号可以被视为分段。该方法采用死区模型来描述间隙，并采用弹性双质量模型来表示伺服系统。考虑到响应速度和微分噪声，选择电机速度作为反馈信号。应特别注意延迟元件和继电器元件参数的选择。这是因为被低估的选择可能会导致系统混乱，从而识别失败。由于关于潜在间隙大小的知识很少，因此迭代地执行识别过程，直到所识别的值收敛到其真实值。这种新的策略只需要一个编码器的电机侧，从它的位置和速度信号可以获得。然而，为了完成识别过程，需要了解电机的转动惯量和负载的转动惯量。仿真和实验结果表明，该策略简单、快速，具有较高的精度。
This paper presents a novel method of backlash identification for PMSM servo system based on a relay feedback technique. We develop this method by analyzing the motor velocity signal in time domain under a strong assumption that the speed signal can be viewed as piecewise segments. The proposed approach takes a dead-zone model to describe the backlash and adopts an elastic two-mass model to represent the servo system. In view of response speed and differential noise, the motor velocity has been chosen to be the feedback signal. It should be pointed that particular attention ought to be paid to the choices of the parameters of the delay element and the relay component. This is because undervalued choices may lead to system chaos and thus the failure of identification. Since little knowledge is available about the potential backlash size, the identification procedure is performed iteratively until the identified value converges to its true value. This new strategy only requires one encoder on the motor side, from which the position and speed signals can be acquired. To complete the identification process, however, knowledge of both the motor’s moment of inertia and the load’s moment of inertia is needed. Simulation and experimental results validate that this new strategy is easy and fast to execute with good accuracy.
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DOI：
10.1002/acs.2437
发表时间：
2014-11
影响因子：
3.1
作者：
J. Reyland;E. Bai
通讯作者：
J. Reyland;E. Bai
影响因子：
5.6
作者：
Qianyan Shen;F. Ding
通讯作者：
Qianyan Shen;F. Ding
DOI：
10.1109/tnnls.2013.2283879
发表时间：
2014-05
影响因子：
10.4
作者：
Huanqing Wang;Bing Chen;Kefu Liu;Xiaoping Liu;Chong Lin
通讯作者：
Huanqing Wang;Bing Chen;Kefu Liu;Xiaoping Liu;Chong Lin
影响因子：
5.4
作者：
Chengpu Yu;Cisheng Zhang;Lihua Xie
通讯作者：
Chengpu Yu;Cisheng Zhang;Lihua Xie
影响因子：
2.8
作者：
J. Chen;X. Lu;Ruifeng Ding
通讯作者：
J. Chen;X. Lu;Ruifeng Ding