.png){kind=icon}
.png){kind=icon}
Return
Optimization algorithms for bilinear model-based predictive control problems
.png){kind=icon}
PRE
AI
DOI：10.1002/aic.10122.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Model-based predictive control (MPC) for discrete-time bilinear state-space models is considered. The optimization problem of the bilinear MPC algorithm is nonlinear in general. It is demonstrated that the structural properties of the bilinear state-space model provide a way to formulate the nonlinear optimization problem as a sequence of quadratic programming problems that exactly represent the original objective function. The proposed optimization algorithm is compared to one that is based on a linearization about all input trajectory. To benefit from the advantages of both algorithms, a hybrid algorithm is proposed, which outperforms the other two in most cases. The applicability of the proposed bilinear MPC algorithm is demonstrated on a polymerization process. (C) 2004 American Institute of Chemical Engineers.
Keywords:
predictive control
bilinear
discrete time
nonlinear optimization
polymerization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4
Papers:
1.1W
Citations:
2.9W
Organization
No organization information available