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Reinforcement learning‐based control of nonlinear systems: Economic optimization and operational safety.

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Τίτλος: Reinforcement learning‐based control of nonlinear systems: Economic optimization and operational safety.
Συγγραφείς: Cui, Xiaodong, Khodaverdian, Arthur, Christofides, Panagiotis D.
Πηγή: AIChE Journal; Sep2026, Vol. 72 Issue 9, p1-16, 16p
Θεματικοί όροι: Reinforcement learning, Nonlinear systems, Predictive control systems, Lyapunov stability, System safety, Economic efficiency, Chemical process control
Περίληψη: This work develops a reinforcement learning (RL)‐based control framework for nonlinear systems that optimizes economic performance while ensuring operational safety. The proposed method combines a real‐time RL feedback policy with an online supervisory mechanism that enforces prescribed safety and closed‐loop stability conditions, using a Lyapunov‐based economic model predictive controller (LEMPC) as a backup controller when needed. To reduce backup reliance, the RL policy is trained in a constraint‐informed environment with penalties on rejected actions. The control framework is applied to a nonlinear chemical process with an economic objective, operational safety considerations, and bounded disturbances. Simulations show recovery of unsafe initial states, improved economic performance relative to LEMPC and imitation learning, and much lower online computational burden than LEMPC. [ABSTRACT FROM AUTHOR]
Copyright of AIChE Journal is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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  Data: Reinforcement learning‐based control of nonlinear systems: Economic optimization and operational safety.
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  Data: <searchLink fieldCode="AR" term="%22Cui%2C+Xiaodong%22">Cui, Xiaodong</searchLink><br /><searchLink fieldCode="AR" term="%22Khodaverdian%2C+Arthur%22">Khodaverdian, Arthur</searchLink><br /><searchLink fieldCode="AR" term="%22Christofides%2C+Panagiotis+D%2E%22">Christofides, Panagiotis D.</searchLink>
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  Data: AIChE Journal; Sep2026, Vol. 72 Issue 9, p1-16, 16p
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  Data: <searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+systems%22">Nonlinear systems</searchLink><br /><searchLink fieldCode="DE" term="%22Predictive+control+systems%22">Predictive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Lyapunov+stability%22">Lyapunov stability</searchLink><br /><searchLink fieldCode="DE" term="%22System+safety%22">System safety</searchLink><br /><searchLink fieldCode="DE" term="%22Economic+efficiency%22">Economic efficiency</searchLink><br /><searchLink fieldCode="DE" term="%22Chemical+process+control%22">Chemical process control</searchLink>
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  Data: This work develops a reinforcement learning (RL)‐based control framework for nonlinear systems that optimizes economic performance while ensuring operational safety. The proposed method combines a real‐time RL feedback policy with an online supervisory mechanism that enforces prescribed safety and closed‐loop stability conditions, using a Lyapunov‐based economic model predictive controller (LEMPC) as a backup controller when needed. To reduce backup reliance, the RL policy is trained in a constraint‐informed environment with penalties on rejected actions. The control framework is applied to a nonlinear chemical process with an economic objective, operational safety considerations, and bounded disturbances. Simulations show recovery of unsafe initial states, improved economic performance relative to LEMPC and imitation learning, and much lower online computational burden than LEMPC. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of AIChE Journal is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1002/aic.70479
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      – Code: eng
        Text: English
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        PageCount: 16
        StartPage: 1
    Subjects:
      – SubjectFull: Reinforcement learning
        Type: general
      – SubjectFull: Nonlinear systems
        Type: general
      – SubjectFull: Predictive control systems
        Type: general
      – SubjectFull: Lyapunov stability
        Type: general
      – SubjectFull: System safety
        Type: general
      – SubjectFull: Economic efficiency
        Type: general
      – SubjectFull: Chemical process control
        Type: general
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      – TitleFull: Reinforcement learning‐based control of nonlinear systems: Economic optimization and operational safety.
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            – D: 01
              M: 09
              Text: Sep2026
              Type: published
              Y: 2026
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