具有外部扰动的线性系统基于ilc的SMC跟踪控制

IF 6.4 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Rongni Yang;Yingjie Gong;Wojciech Paszke
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引用次数: 0

摘要

迭代学习控制(ILC)以其在固定时间间隔内对具有重复动作的系统实现精确跟踪控制的能力而闻名。然而,追求高精度跟踪和快速收敛的双重目标是学习控制领域持续面临的挑战。为了解决这一问题,本文针对一类具有非重复扰动的离散线性系统设计了一种新的线性控制方法。特别地,ILC中的更新项是受滑模控制(SMC)原理的启发而构建的,这使得学习过程分为两个不同的阶段:快速到达阶段和缓慢滑动阶段。因此,该方法可以保证收敛速度和跟踪性能之间的平衡。此外,为了减小非重复干扰的影响,在所提出的ILC方法中加入了干扰补偿机制。此外,可以使用后续迭代的预测均方根(RMS)误差来确定学习增益的最优值,从而消除了额外调优操作的需要。最后,通过仿真实例验证了该方法的有效性和优越性。从业人员注意:对于机电系统和机器人中的许多机械部件,运动是可重复的。迭代学习控制(ILC)是一种成熟的技术,非常适合于提高此类重复性任务的性能,而无需对传感器反馈质量或控制环路带宽提出过多要求。然而,文献中大多数现有的ILC方法主要集中在提高收敛精度上,而很少关注迭代域的收敛速度,特别是在存在干扰的情况下。本文解决了经典ILC方案的局限性,并从滑模控制(SMC)技术中得到了启发。具体而言,提出了一种新的基于smc的ILC算法,可以在快速收敛和精确跟踪性能之间取得良好的平衡,特别是在迭代变扰动的情况下。此外,还将展示如何确定最佳学习增益。以多轴龙门机器人和注射成型工艺为例,仿真结果支持了理论结果,同时也证明了所提出的ILC策略的有效性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ILC-Based Tracking Control for Linear Systems With External Disturbances via an SMC Scheme
Iterative Learning Control (ILC) is renowned for its capability to achieve precise tracking control for systems with repetitive actions at a fixed time interval. However, pursuing the dual objective of high-precision tracking and rapid convergence is a persistent challenge in the field of learning control. To address this problem, a novel ILC method is designed for a class of discrete-time linear systems subject to non-repetitive disturbances in this paper. Particularly, the updating term in ILC is constructed inspired by the principle of sliding mode control (SMC), which results in the learning process being divided into two distinct stages: a rapid reaching stage and a slow sliding stage. As a result, a balance between convergence speed and tracking performance can be ensured via the proposed ILC method. In addition, to attenuate the effects of non-repetitive disturbances, the disturbance compensation mechanism is integrated into the proposed ILC method. Moreover, the optimal value of the learning gain can be determined using the predicted root mean square (RMS) errors of subsequent iterations, eliminating the need for additional tuning actions. Finally, simulation examples are provided to validate the effectiveness and superiority of the proposed new ILC method. Note to Practitioners—For many mechanical components in mechatronic systems and robotics, the motions are repeatable. Iterative learning control (ILC) is a well-established technique ideally suited for enhancing the performance of such repetitive tasks without excessive requirements on sensor-feedback quality or control-loop bandwidth. However, most existing ILC approaches in the literature primarily focus on improving convergence accuracy, while little attention is paid to convergence speed in the iteration domain, especially in the presence of disturbances. This paper addresses the limitations of classical ILC schemes, and draws inspiration from the sliding mode control (SMC) technique. To be specific, a novel SMC-based ILC algorithm is proposed that allows to achieve a good balance between the fast convergence and precise tracking performance, especially in case of iteration variant disturbances. Also, it will be shown how the optimal learning gains can be determined. Base on the examples of multi-axis gantry robot and injection molding process, simulations support the theoretical results, and meanwhile show the effectiveness and advantage of the proposed ILC strategy.
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来源期刊
IEEE Transactions on Automation Science and Engineering
IEEE Transactions on Automation Science and Engineering 工程技术-自动化与控制系统
CiteScore
12.50
自引率
14.30%
发文量
404
审稿时长
3.0 months
期刊介绍: The IEEE Transactions on Automation Science and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. T-ASE welcomes results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, security, service, supply chains, and transportation. T-ASE addresses a research community willing to integrate knowledge across disciplines and industries. For this purpose, each paper includes a Note to Practitioners that summarizes how its results can be applied or how they might be extended to apply in practice.
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