PID Control Law for Trajectory Tracking Error Using Time-Delay Adaptive Neural Networks for Chaos Synchronization
This paper presents an application of Time- Delay adaptive neural networks based on a dynamic neural network for trajectory tracking of unknown nonlin- ear plants. Our approach is based on two main method- ologies: the first one employs Time-Delay neural net- works and Lyapunov-Krasovskii functions...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2015 |
| País: | México |
| Institución: | Universidad Autónoma de Nuevo León |
| Repositorio: | Redalyc-UANL |
| OAI Identifier: | oai:redalyc.org:61539886015 |
| Acceso en línea: | https://www.redalyc.org/articulo.oa?id=61539886015 |
| Access Level: | acceso abierto |
| Palabra clave: | Computación time Lyapunov PID control |
| Sumario: | This paper presents an application of Time- Delay adaptive neural networks based on a dynamic neural network for trajectory tracking of unknown nonlin- ear plants. Our approach is based on two main method- ologies: the first one employs Time-Delay neural net- works and Lyapunov-Krasovskii functions and the sec- ond one is Proportional-Integral-Derivative (PID) control for nonlinear systems. The proposed controller structure is composed of a neural identifier and a control law defined by using the PID approach. The new control scheme is applied via simulations to Chaos Synchroniza- tion. Experimental results have shown the usefulness of the proposed approach for Chaos Production. To verify the analytical results, an example of a dynamical network is simulated and a theorem is proposed to ensure the tracking of the nonlinear system. |
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