Linear programming and model predictive control

Christopher V. Rao, James B. Rawlings

Research output: Contribution to journalConference articlepeer-review


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 computational 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.

Original languageEnglish (US)
Pages (from-to)283-289
Number of pages7
JournalJournal of Process Control
Issue number2
StatePublished - Apr 2000
Externally publishedYes
EventThe 5th IFAC Symposium on the Dynamics and Control of Process Systems (DYCOPS-5) - Corfu, Greece
Duration: Jun 8 1998Jun 10 1998

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Computer Science Applications
  • Industrial and Manufacturing Engineering


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