Simultaneous state and fault estimation based on improved adaptive Kalman filter
| Autor: | Bo Ding, Sutong Li, Tianping Zhang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer simulation
Computer science 010401 analytical chemistry 02 engineering and technology Numerical models Kalman filter 021001 nanoscience & nanotechnology Fault (power engineering) 01 natural sciences 0104 chemical sciences Exponential function symbols.namesake Control theory Gaussian noise symbols State (computer science) 0210 nano-technology |
| Zdroj: | 2020 Chinese Automation Congress (CAC). |
| DOI: | 10.1109/cac51589.2020.9326622 |
| Popis: | This paper presents an improved adaptive Kalman filter (AKF) to obtain state and fault estimation for linear time-varying (LTV) discrete stochastic systems. In the stochastic framework, the fault is modeled as parameter change, then the improved AKF is introduced to estimate state and fault. The exponential convergency of the improved AKF is proved. Numerical simulation is used to verify the proposed approach. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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