Model-Free Robotic Architecture with Task-Multiplexer and Discrete-Time Adaptive Control

Robotic systems equipped with a task-multiplexer unit are considered as a class of unknown non-linear discrete-time systems, where the input is a command voltage of the driver unit and the output is the feedback signal obtained by the multiplexer unit. With only the input and output data available,...

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Main Author: Chidentree Treesatayapun
Format: Article
Language:English
Published: MDPI AG 2019-04-01
Series:Applied System Innovation
Subjects:
Online Access:https://www.mdpi.com/2571-5577/2/2/12
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author Chidentree Treesatayapun
author_facet Chidentree Treesatayapun
author_sort Chidentree Treesatayapun
collection DOAJ
description Robotic systems equipped with a task-multiplexer unit are considered as a class of unknown non-linear discrete-time systems, where the input is a command voltage of the driver unit and the output is the feedback signal obtained by the multiplexer unit. With only the input and output data available, an equivalent identification is formulated by a multi-input fuzzy rule emulated network. An online-learning algorithm is proposed to tune all adjustable parameters by using convergence analysis. Using the equivalent model, a controller is developed when the convergence of the tracking error and internal signals can be guaranteed. An experimental system validates the performance of the proposed scheme. Furthermore, the comparative results are also included, to demonstrate the advantage of the proposed controller.
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spelling doaj.art-8ad1cdca349b49b793e64049059967d62022-12-22T03:15:21ZengMDPI AGApplied System Innovation2571-55772019-04-01221210.3390/asi2020012asi2020012Model-Free Robotic Architecture with Task-Multiplexer and Discrete-Time Adaptive ControlChidentree Treesatayapun0Department of Robotic and Advanced Manufacturing, CINVESTAV-IPN, No. 1062, Parque Industrial Ramos Arizpe, Ramos Arizpe 25903, Coahuila, MexicoRobotic systems equipped with a task-multiplexer unit are considered as a class of unknown non-linear discrete-time systems, where the input is a command voltage of the driver unit and the output is the feedback signal obtained by the multiplexer unit. With only the input and output data available, an equivalent identification is formulated by a multi-input fuzzy rule emulated network. An online-learning algorithm is proposed to tune all adjustable parameters by using convergence analysis. Using the equivalent model, a controller is developed when the convergence of the tracking error and internal signals can be guaranteed. An experimental system validates the performance of the proposed scheme. Furthermore, the comparative results are also included, to demonstrate the advantage of the proposed controller.https://www.mdpi.com/2571-5577/2/2/12robotic systemsdiscrete-time adaptive controlfuzzy neural networksdata-driven identification
spellingShingle Chidentree Treesatayapun
Model-Free Robotic Architecture with Task-Multiplexer and Discrete-Time Adaptive Control
Applied System Innovation
robotic systems
discrete-time adaptive control
fuzzy neural networks
data-driven identification
title Model-Free Robotic Architecture with Task-Multiplexer and Discrete-Time Adaptive Control
title_full Model-Free Robotic Architecture with Task-Multiplexer and Discrete-Time Adaptive Control
title_fullStr Model-Free Robotic Architecture with Task-Multiplexer and Discrete-Time Adaptive Control
title_full_unstemmed Model-Free Robotic Architecture with Task-Multiplexer and Discrete-Time Adaptive Control
title_short Model-Free Robotic Architecture with Task-Multiplexer and Discrete-Time Adaptive Control
title_sort model free robotic architecture with task multiplexer and discrete time adaptive control
topic robotic systems
discrete-time adaptive control
fuzzy neural networks
data-driven identification
url https://www.mdpi.com/2571-5577/2/2/12
work_keys_str_mv AT chidentreetreesatayapun modelfreeroboticarchitecturewithtaskmultiplexeranddiscretetimeadaptivecontrol