Robust model predictive control with integral sliding mode in continuous-time sampled-data nonlinear systems

Matteo Rubagotti, Davide Martino Raimondo, Antonella Ferrara, Lalo Magni

Research output: Contribution to journalArticle

95 Citations (Scopus)

Abstract

This paper proposes a control strategy for nonlinear constrained continuous-time uncertain systems which combines robust model predictive control (MPC) with sliding mode control (SMC). In particular, the so-called Integral SMC approach is used to produce a control action aimed to reduce the difference between the nominal predicted dynamics of the closed-loop system and the actual one. In this way, the MPC strategy can be designed on a system with a reduced uncertainty. In order to prove the stability of the overall control scheme, some general regional input-to-state practical stability results for continuous-time systems are proved.

Original languageEnglish
Article number5570914
Pages (from-to)556-570
Number of pages15
JournalIEEE Transactions on Automatic Control
Volume56
Issue number3
DOIs
Publication statusPublished - Mar 1 2011
Externally publishedYes

Keywords

  • Constrained control
  • nonlinear predictive control (NPC)
  • sampled data control
  • sliding mode control (SMC)
  • stability of nonlinear systems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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