### Abstract

Language | English |
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Title of host publication | Proceedings of the 2016 IEEE International Symposium on Intelligent Control (ISIC), 20-22 september 2016, Buenos Aires, Argentina |

Place of Publication | Piscataway |

Publisher | Institute of Electrical and Electronics Engineers |

Pages | 354-359 |

DOIs | |

State | Published - 2016 |

Event | 2016 IEEE International Symposium on Intelligent Control - Buenos Aires, Argentina Duration: 20 Sep 2016 → 22 Sep 2016 |

### Conference

Conference | 2016 IEEE International Symposium on Intelligent Control |
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Abbreviated title | ISIC |

Country | Argentina |

City | Buenos Aires |

Period | 20/09/16 → 22/09/16 |

### Fingerprint

### Cite this

*Proceedings of the 2016 IEEE International Symposium on Intelligent Control (ISIC), 20-22 september 2016, Buenos Aires, Argentina*(pp. 354-359). Piscataway: Institute of Electrical and Electronics Engineers. DOI: 10.1109/ISIC.2016.7579987

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*Proceedings of the 2016 IEEE International Symposium on Intelligent Control (ISIC), 20-22 september 2016, Buenos Aires, Argentina.*Institute of Electrical and Electronics Engineers, Piscataway, pp. 354-359, 2016 IEEE International Symposium on Intelligent Control, Buenos Aires, Argentina, 20/09/16. DOI: 10.1109/ISIC.2016.7579987

**MPC for linear parameter-varying systems in input-output representation.** / Hanema, J.; Tóth, R.; Lazar, M.; Abbas, H.S.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - MPC for linear parameter-varying systems in input-output representation

AU - Hanema,J.

AU - Tóth,R.

AU - Lazar,M.

AU - Abbas,H.S.

PY - 2016

Y1 - 2016

N2 - In this paper, we propose a method for model predictive control of linear parameter-varying (LPV) systems described in an input-output (IO) representation and subject to input- and output constraints. By assuming exact knowledge of the future trajectory of the scheduling variable, the on-line computations reduce to the solution of a nominal predictive control problem. An incremental non-minimal state-space representation is used as a prediction model, giving a controller with integral action suitable for tracking piecewise-constant reference signals. Closed-loop asymptotic stability is guaranteed by a terminal cost and terminal set constraint, and the computation of an ellipsoidal terminal set is discussed. Numerical examples demonstrate the properties of the proposed approach. When exact future knowledge of the scheduling variable is not available, we argue and show that good practical performance can be obtained by a scheduling prediction strategy.

AB - In this paper, we propose a method for model predictive control of linear parameter-varying (LPV) systems described in an input-output (IO) representation and subject to input- and output constraints. By assuming exact knowledge of the future trajectory of the scheduling variable, the on-line computations reduce to the solution of a nominal predictive control problem. An incremental non-minimal state-space representation is used as a prediction model, giving a controller with integral action suitable for tracking piecewise-constant reference signals. Closed-loop asymptotic stability is guaranteed by a terminal cost and terminal set constraint, and the computation of an ellipsoidal terminal set is discussed. Numerical examples demonstrate the properties of the proposed approach. When exact future knowledge of the scheduling variable is not available, we argue and show that good practical performance can be obtained by a scheduling prediction strategy.

U2 - 10.1109/ISIC.2016.7579987

DO - 10.1109/ISIC.2016.7579987

M3 - Conference contribution

SP - 354

EP - 359

BT - Proceedings of the 2016 IEEE International Symposium on Intelligent Control (ISIC), 20-22 september 2016, Buenos Aires, Argentina

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

ER -