Gain-scheduling offline robust predictive controllers for discrete-time systems with varying parameters
4
Issued Date
2022-02-01
Resource Type
ISSN
2195268X
eISSN
21952698
Scopus ID
2-s2.0-85105281371
Journal Title
International Journal of Dynamics and Control
Volume
10
Issue
1
Start Page
260
End Page
269
Rights Holder(s)
SCOPUS
Bibliographic Citation
International Journal of Dynamics and Control Vol.10 No.1 (2022) , 260-269
Suggested Citation
Jaherng T., Kheawhom S., Bumroongsri P. Gain-scheduling offline robust predictive controllers for discrete-time systems with varying parameters. International Journal of Dynamics and Control Vol.10 No.1 (2022) , 260-269. 269. doi:10.1007/s40435-021-00805-4 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84605
Title
Gain-scheduling offline robust predictive controllers for discrete-time systems with varying parameters
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
This paper presents a synthesis approach of gain-scheduling offline robust predictive controller (GSORPC) for discrete-time systems with varying parameters. The online controller gain is computed by interpolating between feedback gains of the polyhedral invariant (PI) sets constructed for each vertex of the polytopic uncertainty set so robust stability can be guaranteed as the parameters vary. At each time step, the smallest PI set containing the state is selected for each PI sets sequence. Then, the scheduling parameters are measured and employed in the interpolating between feedback gains of the PI sets. The novelty lies in the fact that the online controller gain is scheduled according to the scheduling parameters so the proposed GSORPC can achieve less conservative results as compared with the conventional offline predictive controller. All optimization problems are solved offline so the online computation is tractable.