科研之友
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摘要
Reliable state estimation is challenging for nonlinear hybrid systems. Particle filtering has emerged as an appealing approach for online hybrid state estimation. Mode detection in nonlinear hybrid systems is, however, a troublesome issue for the conventional particle filter mainly due to sample impoverishment. The problem is also exacerbated when dynamics that govern healthy or faulty modes are close together. False mode detection consequently leads to erroneous continuous state estimation. This paper proposes a novel fuzzy-based particle filter to reduce continuous state estimation errors due to failures in mode detection. It is fulfilled by considering a fuzzified contribution of each feasible mode in overall estimation. In addition, two new resampling strategies are presented to tackle the degeneracy problem. A set of simulation test studies are conducted to extract the characteristic features and evaluate the performance of the proposed algorithm compared to observation and transition-based most likely modes tracking particle filter (OTPF) as one of the most meticulous proposed estimation algorithms. The simulation results demonstrate the superior efficiency of the algorithm in dealing with the considered potential estimation problems.
- 出版日期2012-7
- 单位中国石油大学（北京）
