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Online Machine Learning Modeling and Predictive Control of Nonlinear Systems With Scheduled Mode Transitions
  • Cheng Hu,
  • Yuan Cao,
  • Zhe Wu
Cheng Hu
National University of Singapore
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Yuan Cao
The University of Hong Kong
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Zhe Wu
National University of Singapore
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Abstract

This work develops a model predictive control (MPC) scheme using online learning of recurrent neural network (RNN) models for nonlinear systems switched between multiple operating regions following a prescribed switching schedule. Specifically, an RNN model is initially developed offline to model process dynamics using the historical operational data collected in a small region around a certain steady-state. After the system is switched to another operating region under a Lyapunov-based MPC with suitable constraints to ensure satisfaction of the prescribed switching schedule policy, RNN models are updated using real-time process data to improve closed-loop performance. A generalization error bound is derived for the updated RNN models using the notion of regret, and closed-loop stability results are established for the switched nonlinear system under RNN-based MPC. Finally, a chemical process example with the operation schedule that requires switching between two steady-states is used to demonstrate the effectiveness of the proposed RNN-MPC scheme.
20 Jun 2022Submitted to AIChE Journal
20 Jun 2022Assigned to Editor
20 Jun 2022Submission Checks Completed
01 Jul 2022Reviewer(s) Assigned
07 Aug 2022Editorial Decision: Revise Minor
11 Aug 20221st Revision Received
12 Aug 2022Assigned to Editor
12 Aug 2022Submission Checks Completed