### Abstract

Language | English |
---|---|

Title of host publication | Proceedings of the 55th Conference on Decision and Control, 12-14 December 2016, Las Vegas, USA |

Pages | 1458-1463 |

DOIs | |

State | Published - 2016 |

Event | 55th IEEE Conference on Decision and Control (CDC 2016) - Aria Resort and Casino, Las Vegas, United States Duration: 12 Dec 2016 → 14 Dec 2016 Conference number: 55 http://cdc2016.ieeecss.org/ |

### Conference

Conference | 55th IEEE Conference on Decision and Control (CDC 2016) |
---|---|

Abbreviated title | CDC02016 |

Country | United States |

City | Las Vegas |

Period | 12/12/16 → 14/12/16 |

Internet address |

### Fingerprint

### Cite this

*Proceedings of the 55th Conference on Decision and Control, 12-14 December 2016, Las Vegas, USA*(pp. 1458-1463). DOI: 10.1109/CDC.2016.7798472

}

*Proceedings of the 55th Conference on Decision and Control, 12-14 December 2016, Las Vegas, USA.*pp. 1458-1463, 55th IEEE Conference on Decision and Control (CDC 2016), Las Vegas, United States, 12/12/16. DOI: 10.1109/CDC.2016.7798472

**Tube-based anticipative model predictive control for linear parameter-varying systems.** / Hanema, Jurre; Tóth, Roland; Lazar, Mircea.

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

TY - GEN

T1 - Tube-based anticipative model predictive control for linear parameter-varying systems

AU - Hanema,Jurre

AU - Tóth,Roland

AU - Lazar,Mircea

PY - 2016

Y1 - 2016

N2 - Currently available model predictive control methods for linear parameter-varying systems assume that the future behavior of the scheduling trajectory is unknown over the prediction horizon. In this paper, an anticipative tube MPC algorithm for polytopic linear parameter-varying systems under full state feedback is developed. In contrast to existing approaches, the method explicitly takes into account expected future variations in the scheduling variable: its current value is measured exactly, while the future values over the prediction horizon are assumed to belong to a sequence of sets describing expected deviations from a nominal trajectory. Through this mechanism, the controller “anticipates” upon future changes in the system dynamics. The algorithm constructs a tube homothetic to a terminal set and employs gain scheduled vertex control laws. A worst-case cost is minimized: the corresponding optimization problem is a single linear program with complexity linear in the prediction horizon. Numerical examples show the validity of the approach.

AB - Currently available model predictive control methods for linear parameter-varying systems assume that the future behavior of the scheduling trajectory is unknown over the prediction horizon. In this paper, an anticipative tube MPC algorithm for polytopic linear parameter-varying systems under full state feedback is developed. In contrast to existing approaches, the method explicitly takes into account expected future variations in the scheduling variable: its current value is measured exactly, while the future values over the prediction horizon are assumed to belong to a sequence of sets describing expected deviations from a nominal trajectory. Through this mechanism, the controller “anticipates” upon future changes in the system dynamics. The algorithm constructs a tube homothetic to a terminal set and employs gain scheduled vertex control laws. A worst-case cost is minimized: the corresponding optimization problem is a single linear program with complexity linear in the prediction horizon. Numerical examples show the validity of the approach.

U2 - 10.1109/CDC.2016.7798472

DO - 10.1109/CDC.2016.7798472

M3 - Conference contribution

SN - 978-1-5090-1837-6

SP - 1458

EP - 1463

BT - Proceedings of the 55th Conference on Decision and Control, 12-14 December 2016, Las Vegas, USA

ER -