| Autor: |
Antonello Venturino, Sylvain Bertrand, Cristina Stoica Maniu, Teodoro Alamo, Eduardo F. Camacho |
| Přispěvatelé: |
Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática, Universidad de Sevilla. TEP950: Estimación, predicción, optimización y control, Universidad de Sevilla. TEP116: Automática y robótica industrial |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
2021 60th IEEE Conference on Decision and Control (CDC). |
| Popis: |
This paper focuses on Distributed State Estimation over a peer-to-peer sensor network composed by possible low-computational sensors. We propose a new ℓ-step Neighbourhood Distributed Moving Horizon Estimation technique with fused arrival cost and pre-estimation, improving the accuracy of the estimation, while reducing the computation time compared to other approaches from the literature. Simultaneously, convergence of the estimation error is improved by means of spreading the information amongst neighbourhoods, which comes natural in the sliding window data present in the Moving Horizon Estimation paradigm. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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