.png){kind=icon}
Return
Explicit model predictive control: A connected-graph approach
.png){kind=icon}
PRE
AI
DOI：10.1016/j.automatica.2016.10.005.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The ability to solve model predictive control (MPC) problems of linear time-invariant systems explicitly and offline via multi-parametric quadratic programming (mp-QP) has become a widely used methodology. The most efficient approaches used to solve the underlying mp-QP problem are either based on combinatorial considerations, which scale unfavorably with the number of constraints, or geometrical considerations, which require heuristic tuning of the step-size and correct identification of the active set. In this paper, we describe a novel algorithm which unifies these two types of approaches by showing that the solution of a mp-QP problem is given by a connected graph, where the nodes correspond to the different optimal active sets over the parameter space. Using an extensive computational study, as well as the explicit MPC solution of a combined heat and power system, the merits of the proposed algorithm are clearly highlighted. (C) 2016 Elsevier Ltd. All rights reserved.
Keywords:
Explicit MPC
Multi-parametric programming
Combined heat and power
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.9
Papers:
1.2W
Citations:
5.2W
Organization
Cited Papers
ShareSaveShareSaveShareSaveShareSaveExplicit/multi-parametric model predictive control (MPC) of linear discrete-time systems by dynamic and multi-parametric programming.png)
AUTOMATICA
AUTOMATICAIF5.9
PREAI
ShareSaveEnumeration-based approach to solving parametric linear complementarity problemsAUTOMATICA
AUTOMATICAIF5.9
PREAI
ShareSaveShareSave
View more