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$mu$-Synthesis-Based Adaptive Robust Control of Linear Motor Driven Stages With High-Frequency Dynamics: A Case Study

机译: $ mu $ -基于合成的具有高频动力学的线性电动机驱动级的自适应鲁棒控制:一个案例研究

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摘要

Existing control approaches for the precision motion control of linear motor driven systems are mostly based on rigid-body dynamics of the system. Since all drive systems are subjected to the effect of structural flexible modes of their mechanical parts, the neglected high-frequency dynamics resulting from these structural modes have become the main limiting factor when pushing for better tracking performance and higher closed-loop control bandwidth. In this paper, physical modeling and dynamic analysis that take into account the flexibility of the ball bearings between the stage and the linear guideways are presented with experimental verification. With the gained knowledge of these high-frequency dynamics, a novel -synthesis-based adaptive robust control strategy is subsequently developed. The proposed control algorithm uses adaptive model compensation having accurate online parameter estimation to effectively deal with various nonlinearity effects and to transform the difficult trajectory tracking control problem into a robust stabilization problem. The well-developed -synthesis-based linear robust control technique is then employed in the fast feedback control loop design to explicitly deal with the robust control issue associated with the high-frequency dynamics to achieve higher closed-loop bandwidth for better disturbance rejection. Comparative experiments have been performed and the results show the better tracking performance of the proposed algorithm over existing ones.
机译:用于线性电动机驱动系统的精确运动控制的现有控制方法主要基于系统的刚体动力学。由于所有驱动系统都受到其机械零件的结构灵活模式的影响,因此,在寻求更好的跟踪性能和更高的闭环控制带宽时,由这些结构模式引起的被忽略的高频动力学已成为主要的限制因素。在本文中,考虑了工作台和线性导轨之间的滚珠轴承灵活性的物理建模和动态分析均通过实验验证进行了介绍。利用这些高频动力学的知识,随后开发了一种新型的基于合成的自适应鲁棒控制策略。所提出的控制算法使用具有精确在线参数估计的自适应模型补偿来有效处理各种非线性效应,并将困难的轨迹跟踪控制问题转化为鲁棒的稳定问题。然后,在快速反馈控制环路设计中采用成熟的基于合成的线性鲁棒控制技术,以明确处理与高频动力学相关的鲁棒控制问题,以实现更高的闭环带宽,从而更好地抑制干扰。进行了比较实验,结果表明所提出算法的跟踪性能优于现有算法。

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