双质量共振系统的转矩控制:仿真与动态分析

M. Zeinali, S. M. Zanjani, S. Yaghoubi, Amir H. Mosavi, Arman Fathollahi
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引用次数: 0

摘要

多质量系统是由几个质量组成的机械系统,如电机、负载和齿轮,它们通过一个柔性轴连接起来。在电机和被驱动装置之间具有柔性耦合的驱动器中,机械波动不能再像过去对速度控制动力学要求较低时那样被忽视。这些先决条件与伺服驱动器的转速和位置的调节有关,并通过负载转矩阶跃变化引起的速度阶跃响应时间和速度故障消除时间的大小来量化,这些在现代驱动器中是可以测量的。在本研究中,利用特征值分析研究了具有三项转矩和速度控制的双质量谐振系统的动力学行为。所提出的控制策略旨在消除电机轴的旋转波动,抑制负载转矩扰动的影响,在避免负载转速增加的同时对基本转速的变化提供快速响应,并具有抗不稳定的弹性。最后,数值结果验证了该控制器的应用对改善双质量测试系统的动态特性的效果。结果表明,所提控制方案的有效性高度依赖于系统参数。由于多质量系统固有的参数不确定性,建议在未来的工作中使用基于人工神经网络的参数估计器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Torque Control in a Two-Mass Resonant System: Simulation and Dynamic Analysis
A multi-mass system is a mechanical system that consists of several masses such as a motor, load, and gear that are connected by a flexible shaft. The mechanical fluctuations in drives with flexible coupling between the motor and the driven device can no longer be ignored, as they could in the past when requirements for speed control dynamics were low. These prerequisites pertain to the regulation of the rotational speed and position of the servo drive and are quantified by the magnitude of speed step response time and speed fault elimination time caused by the step change of load torque, which, in modern drives, can be measured. In this study, the dynamical behavior of a two-mass resonant system with a three-term controller for control of torque and speed is investigated using eigenvalues analysis. The proposed control strategy aims to eliminate the rotational fluctuations of the motor shaft, dampen the load torque disturbance impact, provide a quick response to changes in the base speed while avoiding increases in the load speed, and be resilient to instability. Finally, numerical outcomes demonstrate the effect of the presented controller application on improving the dynamic demeanor of the two-mass test system. Based on the outcomes, the effectiveness of the proposed control scheme is highly dependent on system parameters. Due to the inherent parameter uncertainty in the multi-mass system, the use of parameter estimators based on artificial neural networks (ANNs) is suggested for future work.
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