约束过程自寻优控制中受控变量选择的智能方法

IF 3.2 Q2 AUTOMATION & CONTROL SYSTEMS
H. Su, Chenchen Zhou, Yi Cao, Shuang-hua Yang, Zuzhen Ji
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引用次数: 4

摘要

摘要自优化控制(SOC)是一种选择适当的受控变量(CV)并保持其恒定以使工厂处于最佳运行状态的技术。自SOC概念提出以来,该主题中的一些棘手挑战仍未解决,例如如何在主动约束集发生变化时选择CV。由于控制结构复杂或局部SOC的限制,以往的工作在处理约束SOC问题时存在结构复杂、控制不准确等缺点,本文提出了一种约束全局SOC(cgSOC)方法来实现自寻优控制变量选择和控制结构设计。可以在非活动和活动之间变化的受约束变量表示为最佳操作下可用测量变量的非线性函数。然后通过神经网络训练在整个操作区域内智能地学习未知函数。然后将非线性函数与实时测量的实际约束变量之间的差用作CV。当CV实时控制在零时,无论何时活动约束发生变化,都可以确保全局接近最佳操作。通过蒸发器案例研究证明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An intelligent approach of controlled variable selection for constrained process self-optimizing control
ABSTRACT Self-optimizing control (SOC) is a technique for selecting appropriate controlled variables (CVs) and maintaining them constant such that the plant runs at its best. Some tough challenges in this subject, such as how to select CVs when the active constraint set changes remains unsolved since the notion of SOC was presented. Previous work had some drawbacks such as structural complexity and control inaccuracy when dealing with constrained SOC problems due to the elaborate control structures or the limitation of local SOC. In order to overcome the deficiency of previous methods, this paper developed a constrained global SOC (cgSOC) approach to implement self-optimizing controlled variable selection and control structure design. The constrained variables that may change between inactive and active are represented as a nonlinear function of available measurement variables under optimal operations. The unknown function is then intelligently learnt over the whole operating region through neural network training. The difference between the nonlinear function and the actual constrained variables measured in real-time is then used as CVs. When the CVs are controlled at zero in real-time, near-optimal operation can be ensured globally whenever active constraint changes. The efficacy of the proposed approach is demonstrated through an evaporator case study.
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来源期刊
Systems Science & Control Engineering
Systems Science & Control Engineering AUTOMATION & CONTROL SYSTEMS-
CiteScore
9.50
自引率
2.40%
发文量
70
审稿时长
29 weeks
期刊介绍: Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory
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