Discrete-time high-order neural network identifier trained with high-order sliding mode observer and unscented Kalman filter
| Autor: | Miguel Hernandez-Gonzalez, Michael Basin, Esteban A. Hernandez-Vargas |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Observer (quantum physics) Artificial neural network Computer science Cognitive Neuroscience Mode (statistics) 02 engineering and technology Kalman filter Computer Science Applications Identifier Nonlinear system 020901 industrial engineering & automation Discrete time and continuous time Artificial Intelligence Control theory 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing |
| Zdroj: | Neurocomputing. 424:172-178 |
| ISSN: | 0925-2312 |
| Popis: | This paper presents a method to identify an unknown discrete-time nonlinear system, using high-order neural networks and high-order sliding mode algorithms, which may be subject to internal and external disturbances. Based on the information obtained from available system states, a high-order neural network model is proposed to approximate the system dynamics. Neural network weights are trained by means of the unscented Kalman filter and high-order sliding mode observer. A simulation example is included to illustrate effectiveness of the proposed scheme. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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