Identification of a MIMO Twin Rotor System Using an Artificial Neural Network Trained by PSO

Mauricio Toscano, William Montalvo, Cristina Bastidas, Eddye Lino

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

Resumen

Twin Rotor Mimo System (TRMS) is a dynamic system with multiple inputs and multiple nonlinear outputs that simulates the action of a helicopter, in this type of system there is a complexity at the time of describing the operation through a transfer function with conventional methods, due to the development of mathematics. For the identification of this type of systems there are alternative methods such as artificial intelligence, specifically Artificial Neural Networks (ANN). The nonlinear autoregressive network with exogenous inputs (NARX) allows modeling nonlinear dynamic systems because it takes prior values of inputs and outputs in different layers. The weights of this network were improved by particle swarm optimization (PSO), as these were considered as particles to find their best position within the search space. For this identification, a data set relating the input to the output of the TRMS at a given time was used through the MATLAB software with its Neural Network Time Series app library and it was obtained as a result that the output signal of the equipment was similar to the estimated output signal of the neural network, optimizing the computational cost and the training time. The algorithm that was developed has the versatility to identify the response of linear systems.

Idioma originalInglés
Título de la publicación alojadaI+D for Smart Cities and Industry - Proceedings of RITAM 2021
EditoresMarcelo Zambrano Vizuete, Miguel Botto-Tobar, Angela Diaz Cadena, Ana Zambrano Vizuete
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas3-14
Número de páginas12
ISBN (versión impresa)9783031112942
DOI
EstadoPublicada - 2023
Evento2nd International Conference on Technological Research, RITAM 2021 - Virtual, Online
Duración: 27 oct. 202129 oct. 2021

Serie de la publicación

NombreLecture Notes in Networks and Systems
Volumen512 LNNS
ISSN (versión impresa)2367-3370
ISSN (versión digital)2367-3389

Conferencia

Conferencia2nd International Conference on Technological Research, RITAM 2021
CiudadVirtual, Online
Período27/10/2129/10/21

Nota bibliográfica

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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