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Performance Optimization via Sequential Processing for Nonlinear State Estimation of Noisy Systems
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DOI：10.1109/TAC.2021.3095461.png)
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摘要
En 中文
We propose a framework for designing observers for noisy nonlinear systems with global convergence properties and performing robustness and noise sensitivity. This framework comes out from the combination of a state norm estimator with a chain of filters, adaptively tuned by the state norm estimator. The state estimate is sequentially processed through the chain of filters. Each filter contributes to improving, by a certain amount, the estimation error performances of the previous filter in terms of noise sensitivity, and this amount is quantitatively evaluated using a comparison criterion, which considers the ratio of the asymptotic error norm bounds of two consecutive filters in the chain. A recursive algorithm is given for implementing the chain of filters and guaranteeing a sequential error performance optimization process. Simulations show the effectiveness of these chains of filters.
Keyword:
Observers
Convergence
Sensitivity
Noise measurement
Nonlinear systems
Uncertainty
Measurement uncertainty
Noisy systems
nonlinear dynamics
observers
期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
机构
引用论文
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AUTOMATICA
AUTOMATICAIF5.9
PREAI
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