Abstract:Self-optimizing control is a strategy for selecting controlled variables, where the economic objective guides the selection and design of controlled variables, with the expectation that maintaining the controlled variables at constant values can achieve optimization effects, translating the process optimization problem into a process control problem. Currently, self-optimizing control is widely applied to steady-state optimization problems. However, the development of process systems exhibits a trend towards refinement, highlighting the importance of optimizing dynamic processes such as batch processes and grade transitions. This paper formally introduces the self-optimizing control problem for dynamic optimization, termed the dynamic self-optimizing control problem, extending the original definition of self-optimizing control. A novel concept, "dynamic controlled variables" (DCVs), is proposed, and an implicit control policy is presented based on this concept. The paper theoretically analyzes the advantages and generality of DCVs compared to explicit control strategies and elucidates the relationship between DCVs and traditional controllers. Moreover, this paper puts forth a data-driven approach to designing self-optimizing DCVs, which considers DCV design as a mapping identification problem and employs deep neural networks to parameterize the variables. Three case studies validate the efficacy and superiority of DCVs in approximating multi-valued and discontinuous functions, as well as their application to dynamic optimization problems with non-fixed horizons, which traditional self-optimizing control methods are unable to address.
| Subjects: | Optimization and Control (math.OC); Machine Learning (cs.LG); Systems and Control (eess.SY) |
| Cite as: | arXiv:2605.06469 [math.OC] |
| (or arXiv:2605.06469v1 [math.OC] for this version) | |
| https://doi.org/10.48550/arXiv.2605.06469 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | Journal of Process Control, 2024, 138: 103228 |
| Related DOI: | https://doi.org/10.1016/j.jprocont.2024.103228
DOI(s) linking to related resources |
From: Chenchen Zhou [view email]
[v1]
Thu, 7 May 2026 15:56:29 UTC (2,953 KB)
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