Real-Time Optimal State Estimation Scheme With Delayed and Periodic Measurements
| Autor: | Soohee Han, Hakjun Lee, Suwon Kang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Estimation
Scheme (programming language) 0209 industrial biotechnology Optimal estimation Computer science 020208 electrical & electronic engineering 02 engineering and technology Kalman filter Interval (mathematics) Time optimal 020901 industrial engineering & automation Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering State (computer science) Electrical and Electronic Engineering computer Algorithm computer.programming_language |
| Zdroj: | IEEE Transactions on Industrial Electronics. 65:5970-5978 |
| ISSN: | 1557-9948 0278-0046 |
| DOI: | 10.1109/tie.2017.2774731 |
| Popis: | This paper proposes an efficient real-time optimal estimation scheme that uses accurate but delayed measurements obtained periodically from high-performance sensing devices. For real-time optimal estimation, we employ two Kalman filters: one to conventionally estimate the current state and the other to precompute for the future state estimation to be carried out, when a new, accurate but delayed measurement arrives. The precomputing Kalman filter does the necessary computation in advance, for the future state estimation, from the available measurements for distributing the computational burden over time, thereby obtaining an optimal estimate in real time. By optimally incorporating accurate but delayed measurements, the optimality is preserved at all times, without imposing a heavy computational burden in a short sampling time interval. It is demonstrated through experiments that the proposed scheme can significantly improve the estimation performance with the least detriment to the real-time computation and memory size, when delayed and periodic measurements are available. |
| Databáze: | OpenAIRE |
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