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Digital Library of the
European Council for Modelling and Simulation |
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Title: |
A Comparsion Of State Estimation Algorithms
For Hybrid Systems |
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Authors: |
Gan Zhou, Wenquan Feng,
Gautam Biswas, Wenfeng Zhang, XiuMei Guan |
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Published in:
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(2015).ECMS 2015 Proceedings edited
by: Valeri M. Mladenov, Grisha Spasov, Petia Georgieva, Galidiya Petrova, European
Council for Modeling and Simulation. doi:10.7148/2015 ISBN:
978-0-9932440-0-1 29th
European Conference on Modelling and Simulation, Albena (Varna), Bulgaria,
May 26th – 29th,
2015 |
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Citation
format: |
Gan Zhou, Wenquan Feng, Gautam Biswas, Wenfeng Zhang, XiuMei Guan (2015). A Comparsion Of
State Estimation Algorithms For Hybrid Systems, ECMS 2015 Proceedings edited
by: Valeri M. Mladenov, Petia Georgieva, Grisha Spasov, Galidiya Petrova European Council for Modeling and Simulation. doi:10.7148/2015-0312 |
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DOI: |
http://dx.doi.org/10.7148/2015-0312 |
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Abstract: |
The dynamic nature of hybrid systems
involves discrete switching behavior between several operating modes and
continuous plant dynamics governed by continuous models in each mode. State
estimation is a important class of approaches for
online monitoring and diagnosis of hybrid systems, which relies on the
estimation of unknown variables using a filtering approach. Focused hybrid
estimation methods concentrate on most likely system evolution trajectories
based on probabilistic and best-first enumeration. On the other hand,
switched Dynamic Bayesian Networks-based particle filter methods track the
continuous behavior in individual modes of operation, and switch modes when
transition conditions are met. In this paper, we study and compare these two
algorithms. The theoretical analysis and experimental results show the
advantages and disadvantages of both approaches. |
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