.png){kind=icon}
Return
A robust multi-model predictive controller for distributed parameter systems
.png){kind=icon}
OA
AI
DOI：10.1016/j.jprocont.2011.10.008.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this work a robust nonlinear model predictive controller for nonlinear convection-diffusion-reaction systems is presented. The controller makes use of a collection of reduced order approximations of the plant (models) reconstructed on-line by projection methods on proper orthogonal decomposition (POD) basis functions. The model selection and model update step is based on a sufficient condition that determines the maximum allowable process-model mismatch to guarantee stable control performance despite process uncertainty and disturbances. Proofs on the existence of a sequence of feasible approximations and control stability are given. Since plant approximations are built on-line based on actual measurements, the proposed controller can be interpreted as a multi-model nonlinear predictive control (MMPC). The performance of the MMPC strategy is illustrated by simulation experiments on a problem that involves reactant concentration control of a tubular reactor with recycle. (C) 2011 Elsevier Ltd. All rights reserved.
Keywords:
Plant model mismatch
Proper orthogonal decomposition
Controller stability
Projection methods
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.9
Papers:
3.5K
Citations:
7.3K
Organization
Cited Papers
ShareSaveComputation of optimal actuator locations for nonlinear controllers in transport-reaction processes
PREAI
ShareSaveInput to state stability of min-max MPC controllers for nonlinear systems with bounded uncertainties.png)
AUTOMATICA
AUTOMATICAIF5.9
PREAI
ShareSaveShareSaveShareSave
View more