.png){kind=icon}
Return
Fixed complexity solution of partial explicit MPC
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compchemeng.2021.107606.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Solving large-scale optimization problems with numerous constraints and optimization variables is a challenging task. Partial explicit MPC enables solving the large-scale optimization problem efficiently. This paper pushes the idea of partial explicit MPC to the fixed complexity parametric solution. The idea is to replace the polytopic critical regions that have a variable number of half-spaces with the maximal volume inner approximation based on the Chebyshev balls. As the approximation has a fixed and known structure, the memory footprint of the parametric solution is also fixed and known in advance, without the necessity to solve the large-scale optimization problem. This valuable property enables scaling the solution size a priori to meet the requirements of the hardware, where the MPC controller will be installed. The proposed method also dramatically reduced the memory burden of the partial explicit solution. Moreover, the proposed method improves the accuracy of the initialization of the hot-start procedure. (C) 2021 Elsevier Ltd. All rights reserved.
Keywords:
Parametric optimization
Fixed complexity solution
Explicit model predictive control
Journal
C
IF:
3.9
Papers:
8.1K
Citations:
1.7W
Organization
Cited Papers
ShareSaveShareSaveA dynamic programming approach to solving constrained linear-quadratic optimal control problems.png)
AUTOMATICA
OAAI
AUTOMATICAIF5.9
OAAIShareSaveDynamic real-time optimization of distributed MPC systems using rigorous closed-loop prediction
PREAI
ShareSaveShareSave
ShareSave
View more