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Machine Learning-Based  Distributed Model Predictive Control of Nonlinear Processes
  • +1
  • Scarlett Chen,
  • Zhe Wu,
  • David Rincon,
  • Panagiotis Christofides
Scarlett Chen
University of California, Los Angeles
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Zhe Wu
University of California, Los Angeles
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David Rincon
University of California, Los Angeles
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Panagiotis Christofides
University of California, Los Angeles
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Peer review status:ACCEPTED

30 May 2020Submitted to AIChE Journal
02 Jun 2020Submission Checks Completed
02 Jun 2020Assigned to Editor
08 Jun 2020Reviewer(s) Assigned
12 Jul 2020Editorial Decision: Revise Major
15 Jul 20201st Revision Received
20 Jul 2020Submission Checks Completed
20 Jul 2020Assigned to Editor
21 Jul 2020Reviewer(s) Assigned
05 Aug 2020Editorial Decision: Accept

Abstract

This work addresses the design of distributed model predictive control (DMPC) systems for nonlinear processes using machine learning models to predict nonlinear dynamic behavior. Specifically, sequential and iterative distributed model predictive control systems are designed and analyzed with respect to closed-loop stability and performance properties. Extensive open- loop data within a desired operating region are used to develop Long Short-Term Memory (LSTM) recurrent neural network models with a sufficiently small modeling error from the actual nonlinear process model. Subsequently, these LSTM models are utilized in Lyapunov- based DMPC to achieve efficient real-time computation time while ensuring closed-loop state boundedness and convergence to the origin. Using a nonlinear chemical process network exam- ple, the simulation results demonstrate the improved computational efficiency when the process is operated under sequential and iterative DMPCs while the closed-loop performance is very close to the one of a centralized MPC system.