J. Tonne and O. Stursberg, “Constrained Model Predictive Control of Processes with Uncertain Structure Modeled by Jump Markov Linear Systems,” in Variable-Structure Approaches, A. Rauh and L. Senkel, Eds. Cham: Springer International Publishing, 2016, pp. 335–361.

Abstract

Linear systems with abrupt changes in its structure, e.g. caused by component failures of a production system, can be modelled by the use of jump Markov linear systems (JMLS). This chapter proposes a finite horizon model predictive control (MPC) approach for discrete-time JMLS considering input constraints as well as constraints for the expectancy of the state trajectory. For the expected value of the state as well as a quadratic cost criterion, recursive prediction schemes are formulated, which consider dependencies on the input trajectory explicitly. Due to the proposed prediction scheme, the MPC problem can be formulated as a quadratic program (QP) exhibiting low computational effort compared to existing approaches. The resulting properties concerning stability as well as computational complexity are investigated and demonstrated by illustrative simulation studies.

BibTex

@INCOLLECTION{TS16a,
 AUTHOR={J. Tonne and O. Stursberg},
 TITLE={{Constrained Model Predictive Control of Processes with Uncertain Structure Modeled by Jump Markov Linear Systems}},
 BOOKTITLE={Variable-Structure Approaches},
 EDITOR={A. Rauh and L. Senkel},
 PUBLISHER={Springer},
 SERIES={Mathematical Engineering},
 YEAR={2016},
 PAGES={335-361},
 COMMENT={noch nicht gemeldet, ISSN: , ? Normseiten}}

URL

https://link.springer.com/chapter/10.1007/978-3-319-31539-3_12