.png){kind=icon}
Return
Dynamic optimization using adaptive control vector parameterization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compchemeng.2005.02.036.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper we present a method for the optimization of dynamic systems using problem-adapted discretizations. The method is based on the direct sequential or single-shooting approach, where the optimization problem is converted into a nonlinear programming problem by parameterization of the control profiles. A fully adaptive, problem-dependent parameterization is generated by repetitive solution of increasingly refined finite-dimensional optimization problems. In each step of the proposed algorithm, the adaptation is based on a wavelet analysis of the solution profiles obtained in the previous step. The method is applied to several case study problems to demonstrate that the adaptive parameterization is more efficient and robust compared to a uniform parameterization of comparable accuracy. (c) 2005 Elsevier Ltd. All rights reserved.
Keywords:
dynamic optimization
sequential approach
adaptive mesh refinement
wavelets
state path constraints
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
3.9
Papers:
8.1K
Citations:
1.7W
Organization
No organization information available
Cited Papers
ShareSaveShareSaveShareSaveShareSaveShareSave
Neural Tube Defects in Embryos of Diabetic Mice: Role of the Pax-3 Gene and Apoptosis.png)
Diabetes
DiabetesIF0
PREAI
ShareSave
View more