Abstract
Many recently developed data-driven fault estimation methods are restricted to minimum-phase systems so that their practical applications are limited. In this paper, the data-driven fault estimation for non-minimum phase (NMP) systems is studied, for which the main difficulty is that the unstable zeros of an NMP system will result in a growing fault-estimation error. To deal with this problem, the inverse of an NMP system is equivalently formulated as a mixed causal and anti-causal system, and the proposed fault estimator is the sum of a stable causal filter and a stable anti-causal filter. The proposed fault estimator is shown to be asymptotically unbiased and its performance is demonstrated by numerical simulations.
Original language | English |
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Pages (from-to) | 181-187 |
Journal | Automatica |
Volume | 92 |
DOIs | |
Publication status | Published - 2018 |
Keywords
- Data-driven methods
- Fault estimation
- Non-minimum phase systems
- Subspace identification