LPV interpolation modeling and modal-based pole placement control for ball screw drive with dynamic variations

IF 6.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS ISA transactions Pub Date : 2024-07-09 DOI:10.1016/j.isatra.2024.07.009
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Abstract

This paper presents a linear parameter varying (LPV) interpolation modeling method and modal-based pole placement (PP) control strategy for the ball screw drive (BSD) with varying dynamics. The BSD is modeled as a global LPV model with position-load dependence by selecting position and load as scheduling variables. The global LPV model is obtained from local subspace closed-loop identification and LPV interpolation modeling. A modal-based global LPV model is obtained through the similarity transformation. Based on this model, a modal-based LPV PP control strategy is proposed to achieve various modal control. Specifically, a state feedback control structure with an LPV state observer is designed to realize online state estimation and real-time state feedback control of modal state variables which cannot be measured directly. The steady-state error is minimized by introducing an error state space (SS) model with the integral effects. Moreover, the stability of the closed-loop system is analyzed according to the controllable decomposition and principle of separation. It is experimentally demonstrated that the proposed modal-based LPV PP control strategy can effectively achieve precise tracking and outstanding robustness meantime.

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针对具有动态变化的滚珠丝杠传动的 LPV 插补建模和基于模态的极点位置控制。
本文提出了一种线性参数变化(LPV)插值建模方法和基于模态的极点布置(PP)控制策略,用于动态变化的滚珠丝杠驱动器(BSD)。通过选择位置和负载作为调度变量,将 BSD 建模为位置-负载相关的全局 LPV 模型。全局 LPV 模型由局部子空间闭环识别和 LPV 插值建模获得。通过相似性变换可获得基于模态的全局 LPV 模型。基于该模型,提出了一种基于模态的 LPV PP 控制策略,以实现各种模态控制。具体来说,设计了一种带有 LPV 状态观测器的状态反馈控制结构,以实现对无法直接测量的模态状态变量的在线状态估计和实时状态反馈控制。通过引入具有积分效应的误差状态空间(SS)模型,使稳态误差最小化。此外,还根据可控分解和分离原理分析了闭环系统的稳定性。实验证明,所提出的基于模态的 LPV PP 控制策略能有效实现精确跟踪,同时具有出色的鲁棒性。
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来源期刊
ISA transactions
ISA transactions 工程技术-工程:综合
CiteScore
11.70
自引率
12.30%
发文量
824
审稿时长
4.4 months
期刊介绍: ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.
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