Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 18, Issue 2, Pages 1091-1101Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2021.3076471
Keywords
Process control; Heuristic algorithms; Performance evaluation; Informatics; Indexes; Optimal control; Target tracking; Decentralized composite control; industrial systems; optimal operational control (OOC); reinforcement learning; singular perturbation theory (SPT)
Categories
Funding
- National Natural Science Foundation of China [61873272, 62073327, 61973306]
- Open Project Foundation of State Key Laboratory of Synthetical Automation for Process Industries of Northeastern University, China [2019-KF-23-04]
- Natural Science Foundation of Jiangsu Province [BK20200086]
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This article investigates the optimal operational control problem for a class of industrial systems consisting of multiple unit devices. The proposed decentralized composite control scheme, using singular perturbation theory, achieves the desired operational index tracking and disturbance rejection. The online and offline controller design methods are proposed for the slow and fast subsystems, respectively, ensuring optimal control without requiring knowledge of the operational process dynamics.
This article investigates the optimal operational control (OOC) problem for a class of industrial systems consisting of multiple unit devices with fast dynamics and an unknown operational process with slow dynamics. First, the OOC problem is formulated as a noncascade optimal control problem of two-time-scale systems with a novel performance function. Second, using singular perturbation theory, a decentralized composite control scheme is proposed by decomposing the original optimal problem into reduced-order fast and slow subsystem problems. Then, in the framework of reinforcement learning, an online controller design method for the slow subsystem is proposed by using the online measurement, and an offline controller design for the fast subsystem is proposed by using the unit device models. The obtained decentralized composite optimal controller achieves both the desired operational index tracking and disturbance rejection without requiring the dynamics of the operational process. Different from the existing cascade design methods, the proposed approach regulates the unit devices and operational process simultaneously, as well as overcomes the potential high dimensionality and ill-conditioned numerical issues. Finally, a mixed separation thickening process and a numerical example are given to illustrate the presented results.
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