Tracking and constant disturbance rejection of robust constrained LMI-based MPC

Tanet Kato, HeeHyol Lee

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In practical situations, poor control performance due to parameter uncertainty is frequently unavoidable. So, robustness against uncertainty has become a desirable property in any control system, especially one that requires accurate parameters such as Model Predictive Control (MPC). With just a nominal model, MPC fails to stabilize a plant that incorporates parameter uncertainty. Robust constrained LMI-based MPC offers a way to handle parameter uncertainty. Therefore, it is appropriate to extend this particular method to cover more types of problems. This research analyzes and synthesizes reference trajectory tracking extension and constant disturbance rejection extension to robust constrained LMI-based MPC in order to broaden the practicality of MPC to process control. The control laws for both reference trajectory tracking extension and constant disturbance rejection extension are obtained through system augmentation to include error dynamics and integrator dynamics, respectively.

Original languageEnglish
JournalIEEJ Transactions on Electronics, Information and Systems
Volume130
Issue number12
DOIs
Publication statusPublished - 2010

Fingerprint

Disturbance rejection
Model predictive control
Trajectories
Process control
Control systems
Uncertainty

Keywords

  • Constant disturbance rejection
  • Linear matrix inequalities
  • Model predictive control
  • Reference trajectory tracking

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

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AB - In practical situations, poor control performance due to parameter uncertainty is frequently unavoidable. So, robustness against uncertainty has become a desirable property in any control system, especially one that requires accurate parameters such as Model Predictive Control (MPC). With just a nominal model, MPC fails to stabilize a plant that incorporates parameter uncertainty. Robust constrained LMI-based MPC offers a way to handle parameter uncertainty. Therefore, it is appropriate to extend this particular method to cover more types of problems. This research analyzes and synthesizes reference trajectory tracking extension and constant disturbance rejection extension to robust constrained LMI-based MPC in order to broaden the practicality of MPC to process control. The control laws for both reference trajectory tracking extension and constant disturbance rejection extension are obtained through system augmentation to include error dynamics and integrator dynamics, respectively.

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