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Intelligent Model for Endpoint Accelerations of Two link Flexible Manipulator Using a Deep Learning Neural Network

This article investigates a two-link flexible manipulator (TLFM) that can be modelled utilizing a deep learning neural network. The system was classified under a multiple-input multiple-output (MIMO) system. In the modelling stage of this study, the TLFM dynamic models were divided into single-input...

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Main Authors: Zidan, Abdulghani, Intan Z., Mat Darus, Annisa, Jamali
Format: Proceeding
Language:English
Published: 2022
Subjects:
T Technology (General)
Online Access:http://ir.unimas.my/id/eprint/40300/1/Intelligent%20Model.pdf
http://ir.unimas.my/id/eprint/40300/
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spelling my.unimas.ir.403002022-11-01T01:17:04Z http://ir.unimas.my/id/eprint/40300/ Intelligent Model for Endpoint Accelerations of Two link Flexible Manipulator Using a Deep Learning Neural Network Zidan, Abdulghani Intan Z., Mat Darus Annisa, Jamali T Technology (General) This article investigates a two-link flexible manipulator (TLFM) that can be modelled utilizing a deep learning neural network. The system was classified under a multiple-input multiple-output (MIMO) system. In the modelling stage of this study, the TLFM dynamic models were divided into single-input single-output (SISO) models. Since coupling impact was assumed to be minimised, the characterizations of TLFM were defined independently in each model. Two discrete SISO models of a flexible two link manipulator were developed using the torque input and the endpoint accelerations of each link. The input-output data pairs were collected from experimental work and utilised to establish the system model. The Long Short-Term Memory (LSTM) algorithm optimised using Particle Swarm Optimization (PSO) was selected as the model structure due to the system's high degree of nonlinearity. The identification of the TLFM system utilizing LSTM optimised by PSO was successful, according to the high-performance result of PSO. Using LSTM-PSO, it is demonstrated that both link 1 and 2 models are accurately identified and that their performance in terms of MSE for links endpoint acceleration 1 and 2 is within a 95% confidence interval. 2022-10-28 Proceeding PeerReviewed text en http://ir.unimas.my/id/eprint/40300/1/Intelligent%20Model.pdf Zidan, Abdulghani and Intan Z., Mat Darus and Annisa, Jamali (2022) Intelligent Model for Endpoint Accelerations of Two link Flexible Manipulator Using a Deep Learning Neural Network. In: 2022 IEEE 8th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), 27-28 Aug. 2022, Hatten Hotel, Melaka.
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Zidan, Abdulghani
Intan Z., Mat Darus
Annisa, Jamali
Intelligent Model for Endpoint Accelerations of Two link Flexible Manipulator Using a Deep Learning Neural Network
description This article investigates a two-link flexible manipulator (TLFM) that can be modelled utilizing a deep learning neural network. The system was classified under a multiple-input multiple-output (MIMO) system. In the modelling stage of this study, the TLFM dynamic models were divided into single-input single-output (SISO) models. Since coupling impact was assumed to be minimised, the characterizations of TLFM were defined independently in each model. Two discrete SISO models of a flexible two link manipulator were developed using the torque input and the endpoint accelerations of each link. The input-output data pairs were collected from experimental work and utilised to establish the system model. The Long Short-Term Memory (LSTM) algorithm optimised using Particle Swarm Optimization (PSO) was selected as the model structure due to the system's high degree of nonlinearity. The identification of the TLFM system utilizing LSTM optimised by PSO was successful, according to the high-performance result of PSO. Using LSTM-PSO, it is demonstrated that both link 1 and 2 models are accurately identified and that their performance in terms of MSE for links endpoint acceleration 1 and 2 is within a 95% confidence interval.
format Proceeding
author Zidan, Abdulghani
Intan Z., Mat Darus
Annisa, Jamali
author_facet Zidan, Abdulghani
Intan Z., Mat Darus
Annisa, Jamali
author_sort Zidan, Abdulghani
title Intelligent Model for Endpoint Accelerations of Two link Flexible Manipulator Using a Deep Learning Neural Network
title_short Intelligent Model for Endpoint Accelerations of Two link Flexible Manipulator Using a Deep Learning Neural Network
title_full Intelligent Model for Endpoint Accelerations of Two link Flexible Manipulator Using a Deep Learning Neural Network
title_fullStr Intelligent Model for Endpoint Accelerations of Two link Flexible Manipulator Using a Deep Learning Neural Network
title_full_unstemmed Intelligent Model for Endpoint Accelerations of Two link Flexible Manipulator Using a Deep Learning Neural Network
title_sort intelligent model for endpoint accelerations of two link flexible manipulator using a deep learning neural network
publishDate 2022
url http://ir.unimas.my/id/eprint/40300/1/Intelligent%20Model.pdf
http://ir.unimas.my/id/eprint/40300/
_version_ 1748706498628812800
score 13.211869

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