Sampled-Data State Estimation for Complex Networks With Partial Measurements
| Autor: | Chang-Xin Cai, Dan-Dan Zhou, Ding-Xin He, Bin Hu, Ding-Xue Zhang, Zhi-Hong Guan |
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| Rok vydání: | 2020 |
| Předmět: |
Estimation
0209 industrial biotechnology Observer (quantum physics) Computer science 02 engineering and technology Complex network Computer Science Applications Exponential function Human-Computer Interaction 020901 industrial engineering & automation Control and Systems Engineering Control theory 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Fraction (mathematics) State (computer science) State observer Electrical and Electronic Engineering Software |
| Zdroj: | IEEE Transactions on Systems, Man, and Cybernetics: Systems. 50:4787-4795 |
| ISSN: | 2168-2232 2168-2216 |
| DOI: | 10.1109/tsmc.2018.2865097 |
| Popis: | This paper addresses the sampled-data state estimation problem for complex networks using partial nodes’ measurements. A hybrid observer network with partial control is developed to estimate the state information. The key point of the hybrid observer network is that the state observer network is continuous-time by introducing an output predictor. Besides, the hybrid observer only requires a fraction of nodes’ sampled measurements with partial control technique. It reduces the state estimation cost and improves the estimation effectiveness. Some criteria are developed to guarantee that the proposed observer network is an exponential observer. Finally, simulation example validates the proposed approach. |
| Databáze: | OpenAIRE |
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