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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.
|Title of host publication||Proceedings of the 55th Conference on Decision and Control, 12-14 December 2016, Las Vegas, USA|
|Publication status||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
|Conference||55th IEEE Conference on Decision and Control (CDC 2016)|
|Period||12/12/16 → 14/12/16|
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- 1 Contributed talk
Jurre Hanema (Speaker)12 Dec 2016 → 14 Dec 2016
Activity: Talk or presentation types › Contributed talk › Scientific