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 |
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Tube-based anticipative model predictive control for linear parameter-varying systems. / Hanema, Jurre; Tóth, Roland; Lazar, Mircea.
Proceedings of the 55th Conference on Decision and Control, 12-14 December 2016, Las Vegas, USA. 2016. p. 1458-1463.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 -