Event-Based State Estimation for Networked Singularly Perturbed Complex Networks
This paper deals with the multievent-triggering-based state estimation for a class of discrete-time networked singularly perturbed complex networks (SPCNs). A small singularly perturbed scalar is adopted to establish a discrete-time SPCNs model. To reduce the communication burdens, the data transmis...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/6122921 |
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| Summary: | This paper deals with the multievent-triggering-based state estimation for a class of discrete-time networked singularly perturbed complex networks (SPCNs). A small singularly perturbed scalar is adopted to establish a discrete-time SPCNs model. To reduce the communication burdens, the data transmission between the sensor and the estimator is managed by a multievent generator function. Depending on the singularly-perturbed-based Lyapunov theory, a sufficient condition is constructed to guarantee that the estimation error is exponentially ultimately bounded in the mean square. Finally, the validity of the developed result is demonstrated by a simulation example. |
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| ISSN: | 1099-0526 |