Rubagotti, Matteo and Raimondo, Davide Martino and Ferrara, Antonella and Magni, Lalo
*Robust model predictive control with integral sliding mode in continuous-time sampled-data nonlinear systems.*
IEEE Transactions on Automatic Control , 56 (3).
556 -570.
ISSN 0018-9286
(2011)

## 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.

Item Type: | Article |
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Identification Number: | 10.1109/TAC.2010.2074590 |

Projects: | This work was supported in part by the European Commission under the Project Feednetback FP7-ICT-223866 and by the Italian PRIN project “Model predictive control algorithms for artificial pancreas.” |

Uncontrolled Keywords: | closed-loop system; continuous-time sampled-data nonlinear uncertain systems; input-to-state practical stability; integral sliding mode control; robust model predictive control; stability; closed loop systems; continuous time systems; nonlinear control systems; predictive control; robust control; sampled data systems; uncertain systems; variable structure systems |

Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TJ Mechanical engineering and machinery |

Research Area: | Computer Science and Applications |

Depositing User: | Users 37 not found. |

Date Deposited: | 02 Mar 2012 15:30 |

Last Modified: | 30 Sep 2013 12:33 |

URI: | http://eprints.imtlucca.it/id/eprint/1208 |

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