A Probabilistic Approach for Model Following of Markovian Jump Linear Systems Subject to Actuator Saturation
- Authors
- Wang, Linpeng; Zhu, Jin; Park, Junhong
- Issue Date
- Oct-2012
- Publisher
- INST CONTROL ROBOTICS & SYSTEMS, KOREAN INST ELECTRICAL ENGINEERS
- Keywords
- Actuator saturation; Markovian jump linear systems; model following; particle control approach
- Citation
- INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, v.10, no.5, pp.1042 - 1048
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
- Volume
- 10
- Number
- 5
- Start Page
- 1042
- End Page
- 1048
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/164631
- DOI
- 10.1007/s12555-012-0522-2
- ISSN
- 1598-6446
- Abstract
- 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.
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