Constrained Model Predictive Control of Processes with Uncertain Structure Modeled by Jump Markov Linear Systems

  • Jens TonneEmail author
  • Olaf Stursberg
Part of the Mathematical Engineering book series (MATHENGIN)


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.


Markov Jump Linear Systems (JMLS) Model Predictive Control (MPC) Discrete-time JMLS Quadratic Cost Criterion Finite Horizon MPC 
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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Volkswagen AGBaunatalGermany
  2. 2.Department of Electrical Engineering and Computer Science, Institute of Control and System TheoryUniversity of KasselKasselGermany

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