A Probabilistic Approach for Model Following of Markovian Jump Linear Systems Subject to Actuator Saturation

This paper is concerned with the model following problem of Markovian jump linear systems (MJLSs), which suffer from stochastic uncertainties and actuator saturation. By applying a probabilistic approach based on particles, a sequence of control inputs is designed to guarantee that the model followi...

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Bibliographic Details
Published inInternational journal of control, automation, and systems Vol. 10; no. 5; pp. 1042 - 1048
Main Authors Wang, Linpeng, Zhu, Jin, Park, Junhong
Format Journal Article
LanguageKorean
Published 2012
Subjects
model following
Markovian jump linear systems
particle control approach
Actuator saturation
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Summary:This paper is concerned with the model following problem of Markovian jump linear systems (MJLSs), which suffer from stochastic uncertainties and actuator saturation. By applying a probabilistic approach based on particles, a sequence of control inputs is designed to guarantee that the model following error remains within a desired region in a certain probability, as well as the control cost is optimal. Motivated by this, the stochastic control problem is represented by chance constrained programming, and approximated as a determinate optimization one, which is solved by mixed integer linear programming (MILP). Furthermore, an improved particle control approach is proposed to reduce the computation complexity. The effectiveness of this improved approach is demonstrated by an example along with complexity comparison.
Bibliography:KISTI1.1003/JNL.JAKO201213660553837
ISSN:1598-6446
2005-4092