Robust Fault-Tolerant Control for Reentrant Manufacturing Systems With Periodic Faults.

IF 11.3 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Kexin Zhang, Qing Gao, Michael V Basin, Steven X Ding, Jinhu Lu
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引用次数: 0

Abstract

This article investigates the robust fault-tolerant control problem for a class of reentrant manufacturing systems (RMSs) subject to periodic workstation faults. First, an impulsive hyperbolic partial differential equation (IHPDE) continuum model is introduced to characterize the spatiotemporal evolution of work-in-progress (WIP) in RMSs, in which the state jumps caused by workstation faults are explicitly captured. The fault-induced WIP state jumps are further classified into two cases, namely, mixed WIP state jumps and destabilizing WIP state jumps. Then, for each case, a fuzzy-model-based integral sliding mode control approach is developed to achieve fault-tolerant stabilization of the considered RMSs. Under the proposed control schemes, the RMSs affected by periodic machinery failures can robustly and agilely track predicted market demand while maintaining stable production output. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed fault-tolerant control schemes, and the potential Zeno behavior of the closed-loop system is also discussed.

具有周期性故障的可重入制造系统的鲁棒容错控制。
本文研究了一类周期性工作站故障的可重入制造系统(RMSs)的鲁棒容错控制问题。首先,引入脉冲双曲偏微分方程(IHPDE)连续体模型来描述RMSs中在制品(WIP)的时空演化,该模型明确捕获了工作站故障引起的状态跳变。将故障诱导的在制品状态跳变进一步分为混合型和不稳定型两种情况。然后,针对每种情况,开发了基于模糊模型的积分滑模控制方法,以实现所考虑的RMSs的容错镇定。在所提出的控制方案下,受周期性机械故障影响的RMSs能够在保持稳定产量的同时稳健、敏捷地跟踪预测的市场需求。最后,通过数值仿真验证了所提容错控制方案的有效性,并对闭环系统潜在的芝诺行为进行了讨论。
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来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
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