File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1002/aic.17882
- WOS: WOS:000851571700001
Supplementary
-
Citations:
- Web of Science: 0
- Appears in Collections:
Article: Online machine learning modeling and predictive control of nonlinear systems with scheduled mode transitions
Title | Online machine learning modeling and predictive control of nonlinear systems with scheduled mode transitions |
---|---|
Authors | |
Issue Date | 2022 |
Citation | AIChE Journal, 2022 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/322569 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hu, C | - |
dc.contributor.author | Cao, Y | - |
dc.contributor.author | Wu, Z | - |
dc.date.accessioned | 2022-11-14T08:26:57Z | - |
dc.date.available | 2022-11-14T08:26:57Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | AIChE Journal, 2022 | - |
dc.identifier.uri | http://hdl.handle.net/10722/322569 | - |
dc.description.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. | - |
dc.language | eng | - |
dc.relation.ispartof | AIChE Journal | - |
dc.title | Online machine learning modeling and predictive control of nonlinear systems with scheduled mode transitions | - |
dc.type | Article | - |
dc.identifier.email | Cao, Y: yuancao@hku.hk | - |
dc.identifier.authority | Cao, Y=rp02862 | - |
dc.identifier.doi | 10.1002/aic.17882 | - |
dc.identifier.hkuros | 341728 | - |
dc.identifier.isi | WOS:000851571700001 | - |
dc.publisher.place | Wiley Online Library | - |