Neural Network Based on Orthogonal Polynomials Applied in Magnetic Levitation System Control
| Autor: | Miroslav B. Milovanović, Staniša Lj. Perić, Miodrag Spasic, Dragan Antić, Saša S. Nikolić, Marko Milojković |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Engineering Artificial neural network business.industry Activation function 02 engineering and technology Sigmoid function Nonlinear system 020901 industrial engineering & automation Control theory Position (vector) Orthogonal polynomials 0202 electrical engineering electronic engineering information engineering Levitation 020201 artificial intelligence & image processing Electrical and Electronic Engineering Control logic business |
| Zdroj: | Elektronika ir Elektrotechnika. 23 |
| ISSN: | 2029-5731 1392-1215 |
| DOI: | 10.5755/j01.eie.23.3.13167 |
| Popis: | This paper presents a new approach for improving performances of magnetic levitation system. Controlled parameter is the amplitude which levitated object achieves during movement from one levitation position to another. Two position levitation with improved amplitude performances is obtained by implementing orthogonal neural network in standard levitation control logic. Proposed network is a nonlinear autoregressive neural network with newly developed activation function based on orthogonal polynomials. Performed experiments on a system with default control logic showed that it could not provide stable two position levitation when specified amplitude of the levitation object is greater than 10-4 m. Artificial network was trained using real experimental data and it was based on standard tangent and sigmoid activation functions. Default activation functions were then substituted with a newly developed orthogonal polynomial functions. The amplitude 10-3 m was achieved with stable two position levitation after parameter optimization. It is proven that simple control logic with nonlinear autoregressive neural network and proper activation function can provide improved amplitude performances.DOI: http://dx.doi.org/10.5755/j01.eie.23.3.13167 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|