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

Jurre Hanema, Roland Tóth, Mircea Lazar

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

15 Citations (Scopus)
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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.
Original languageEnglish
Title of host publicationProceedings of the 55th Conference on Decision and Control, 12-14 December 2016, Las Vegas, USA
Publication statusPublished - 2016
Event55th IEEE Conference on Decision and Control (CDC 2016) - Aria Resort and Casino, Las Vegas, United States
Duration: 12 Dec 201614 Dec 2016
Conference number: 55


Conference55th IEEE Conference on Decision and Control (CDC 2016)
Abbreviated titleCDC02016
Country/TerritoryUnited States
CityLas Vegas
Internet address


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