Experiments with Neural Networks in the Identification and Control of a Magnetic Levitation System Using a Low-Cost Platform
| Autor: | Bruno E. Silva, Ramiro S. Barbosa |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Computer science Computer Science::Neural and Evolutionary Computation Internal model 02 engineering and technology nonlinear system lead compensator lcsh:Technology lcsh:Chemistry 020901 industrial engineering & automation Control theory 0202 electrical engineering electronic engineering information engineering General Materials Science neural identification MATLAB Instrumentation internal model controller lcsh:QH301-705.5 Magnetic levitation computer.programming_language Fluid Flow and Transfer Processes Nonlinear autoregressive exogenous model magnetic levitation Artificial neural network Quantitative Biology::Neurons and Cognition lcsh:T Process Chemistry and Technology General Engineering neural control lcsh:QC1-999 Computer Science Applications inverse controller lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 Levitation 020201 artificial intelligence & image processing model reference controller lead–lag compensator Lead–lag compensator lcsh:Engineering (General). Civil engineering (General) computer lcsh:Physics |
| Zdroj: | Applied Sciences, Vol 11, Iss 2535, p 2535 (2021) Applied Sciences Volume 11 Issue 6 |
| ISSN: | 2076-3417 |
| Popis: | In this article, we designed and implemented neural controllers to control a nonlinear and unstable magnetic levitation system composed of an electromagnet and a magnetic disk. The objective was to evaluate the implementation and performance of neural control algorithms in a low-cost hardware. In a first phase, we designed two classical controllers with the objective to provide the training data for the neural controllers. After, we identified several neural models of the levitation system using Nonlinear AutoRegressive eXogenous (NARX)-type neural networks that were used to emulate the forward dynamics of the system. Finally, we designed and implemented three neural control structures: the inverse controller, the internal model controller, and the model reference controller for the control of the levitation system. The neural controllers were tested on a low-cost Arduino control platform through MATLAB/Simulink. The experimental results proved the good performance of the neural controllers. |
| Databáze: | OpenAIRE |
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