Multiresolution controller based on WaveNets for nonlinear systems
The design of robust and free-model controllers has been an open problem for many years. Although many approaches have been reported in the literature, the PID controller is the most used for its simplicity and speed response under some conditions. Thus, in many approaches, PID-like controllers are...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | México |
| Institución: | UNIVERSIDAD AUTÓNOMA DEL ESTADO DE HIDALGO |
| Repositorio: | PÄDI Boletín Científico de Ciencias Básicas e Ingeniería del ICBI |
| Idioma: | español |
| OAI Identifier: | oai:repository.uaeh.edu.mx:article/11391 |
| Acceso en línea: | https://repository.uaeh.edu.mx/revistas/index.php/icbi/article/view/11391 |
| Access Level: | acceso abierto |
| Palabra clave: | Multi-resolution analysis Intelligent control Wavelet function Quanser helicopter Neural network Análisis multiresolución Control inteligente Función wavelet Helicóptero Quanser Redes neuronales |
| Sumario: | The design of robust and free-model controllers has been an open problem for many years. Although many approaches have been reported in the literature, the PID controller is the most used for its simplicity and speed response under some conditions. Thus, in many approaches, PID-like controllers are a recurrent controller structure. In recent years, due to the systems carrying more complex tasks subject to different environmental conditions, as in aircraft or robots, artificial intelligence tools such as neural networks have been used to self-tune feedback gain controllers. Based on the wavelet transformation, this paper proposes a multi-resolution proportional controller (PMR) scheme to control non-linear systems, avoiding system information. The PMR decomposes the tracking error to obtain different information on scale and frequency, which allows for compensation for various uncertainties in the system. The wavelet~neural network (WaveNet) information approximates the dynamic input-output model and is used for self-tuning feedback gains. Numerical simulation results for a 2-degree-of-freedom Quanser helicopter are presented, under different conditions, to verify the performance of the PMR controller. |
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