多时间尺度非线性系统的数据-知识驱动集成最优控制

IF 8.7 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Honggui Han;Yue Zhang;Hao-Yuan Sun;Zheng Liu;Junfei Qiao
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引用次数: 0

摘要

在工业过程中,优化和控制过程在不同的时间尺度上运行。忽略多时间尺度特性会导致最优控制律无法保证被控非线性系统的控制性能。针对这一问题,本文提出了一种数据-知识驱动的非线性系统多时间尺度集成最优控制策略。首先,建立了包含时间尺度协同目标函数的多时间尺度综合最优控制框架;然后,将非线性系统的多时间尺度协调到快速时间尺度,以保证实时优化和控制。其次,针对慢时间尺度数据信息驱动的快时间尺度模型预测精度低的问题,引入数据-知识驱动的预测模型来预测系统在快时间尺度下的未来动态。此外,设计了一种知识补偿策略来补充缺失的快速时标特定信息。第三,采用协同优化算法同时求解设定值和控制律。此外,还证明了DK-MTSIOC预测模型的数据和知识驱动的收敛性和稳定性。最后,在一个常规非线性系统和一个污水处理过程的基准实例上对所提出的DK-MTSIOC进行了测试,以验证其有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-Knowledge-Driven Integrated Optimal Control for Multitime Scale Nonlinear Systems
In industrial processes, the optimization and control processes operate on different time scales. Neglecting the multitime scale characteristics can lead to an optimized control law that fails to guarantee the control performance of the controlled nonlinear system. To address this problem, a data-knowledge-driven multitime scale integrated optimal control (DK-MTSIOC) strategy is proposed for the nonlinear system in this article. First, a multitime scale integrated optimal control (MTSIOC) framework, including a time scale collaborative objective function, is established. Then, multitime scales of nonlinear systems are coordinated to the fast time scale to ensure real-time optimization and control. Second, to address the problem of low accuracy in predicting fast time scale model driven by the slow time scale data information, a data-knowledge-driven prediction model is introduced to predict the future dynamics of the system at the fast time scale. Furthermore, a knowledge compensation strategy is designed to supplement missing fast time scale specific information. Third, a collaborative optimization algorithm is utilized to solve the setpoints and control laws simultaneously. Besides, the convergence of the data and knowledge-driven prediction model and stability of DK-MTSIOC are proved. Finally, the proposed DK-MTSIOC is tested on a conventional nonlinear system and a benchmark example of the wastewater treatment process to validate its effectiveness.
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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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