Decreasing-horizon Robust Model Predictive Control with Specified Settling Time to A Terminal Constraint Set

Weilin Yang, Gang Feng, Tie Jun Zhang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Robust model predictive control for discrete-time linear systems with norm-bounded disturbances is investigated in this pape1r. The control objective is to steer the system state t a terminal constraint set within specified number of steps. Meanwhile, the performance of the closed-loop control system is optimized. A decreasing-horizon predictive control strategy is proposed. Moreover, affine state-feedback control laws with memory of prior states are adopted over the prediction horizon. To optimize the system performance, an ∞-type cost function is considered in this paper. It is shown that finite settling time is achieved, if the optimization problem in the proposed control strategy is initially solvable. Some simulations are presented to show the effectiveness of the proposed control strategy.

Original languageBritish English
Pages (from-to)664-673
Number of pages10
JournalAsian Journal of Control
Volume18
Issue number2
DOIs
StatePublished - 1 Mar 2016

Keywords

  • affine feedback controller
  • model predictive control
  • Robustness
  • specified settling time

Fingerprint

Dive into the research topics of 'Decreasing-horizon Robust Model Predictive Control with Specified Settling Time to A Terminal Constraint Set'. Together they form a unique fingerprint.

Cite this