Fuzzy-Model-Based Fault-Tolerant Control for Stochastic Re-Entrant Manufacturing Systems

IF 8.6 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Kexin Zhang;Qing Gao;Steven X. Ding;Jinhu Lü;Jianbin Qiu;Yige Guo
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引用次数: 0

Abstract

This study addresses the problem of guaranteed cost fault-tolerant fuzzy control for multiline re-entrant manufacturing systems (RMSs) against stochastic disturbances and workstation faults. Initially, a nonlinear hyperbolic impulsive partial differential equation model is employed to describe the complex and hybrid dynamics of RMSs suffering from unexpected faults within the working stations, and then the corresponding approximation T-S fuzzy model is constructed. In what follows, with the aid of the parallel distributed compensation fuzzy control scheme, the main results of stability analysis and controller synthesis for the closed-loop re-entrant manufacturing control system are derived using a timer-dependent Lyapunov functional with spatio-temporal auxiliary variables. It is found that by means of the proposed fault-tolerant control approach, the RMS can be effectively and robustly driven to a desired production mode with steady feeding and production rates while the upper bound of a quadratic cost function is minimized. Finally, the effectiveness of the proposed control approach is validated through numerical simulations.
基于模糊模型的随机再进入制造系统容错控制
针对随机干扰和工作站故障,研究多线再入制造系统的保成本容错模糊控制问题。首先采用非线性双曲型脉冲偏微分方程模型来描述工作站内发生意外故障时RMSs的复杂混合动力学,然后建立相应的近似T-S模糊模型。在此基础上,利用具有时空辅助变量的李雅普诺夫函数,利用并行分布式补偿模糊控制方法,推导了闭环再入制造控制系统的稳定性分析和控制器综合的主要结果。结果表明,采用该容错控制方法,可以有效地、鲁棒地将RMS驱动到期望的生产模式,使其具有稳定的投料量和生产速率,同时最小化二次代价函数的上界。最后,通过数值仿真验证了所提控制方法的有效性。
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来源期刊
IEEE Transactions on Systems Man Cybernetics-Systems
IEEE Transactions on Systems Man Cybernetics-Systems AUTOMATION & CONTROL SYSTEMS-COMPUTER SCIENCE, CYBERNETICS
CiteScore
18.50
自引率
11.50%
发文量
812
审稿时长
6 months
期刊介绍: The IEEE Transactions on Systems, Man, and Cybernetics: Systems encompasses the fields of systems engineering, covering issue formulation, analysis, and modeling throughout the systems engineering lifecycle phases. It addresses decision-making, issue interpretation, systems management, processes, and various methods such as optimization, modeling, and simulation in the development and deployment of large systems.
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