Author/Authors :
Christopher V. Rao and James B. Rawlings، نويسنده ,
Title Of Article :
Linear programming and model predictive control
Latin Abstract :
The practicality of model predictive control (MPC) is partially limited by the ability to solve optimization problems in real time.
This requirement limits the viability of MPC as a control strategy for large scale processes. One strategy for improving the com-
putational performance is to formulate MPC using a linear program. While the linear programming formulation seems appealing
from a numerical standpoint, the controller does not necessarily yield good closed-loop performance. In this work, we explore MPC
with an l1 performance criterion. We demonstrate how the non-smoothness of the objective function may yield either dead-beat or
idle control performance.
NaturalLanguageKeyword :
Model predictive control , Linear programming , optimization
JournalTitle :
Studia Iranica
