Tracking of periodic oscillations in an underactuated system via adaptive neural networks

In this paper, the tracking control of periodic oscillations in an underactuated mechanical system is discussed. The proposed scheme is derived from the feedback linearization control technique and adaptive neural networks are used to estimate the unknown dynamics and to compensate uncertainties. Th...

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Main Authors: Sergio A Puga-Guzmán, Carlos Aguilar-Avelar, Javier Moreno-Valenzuela, Víctor Santibáñez
Format: Article
Language:English
Published: SAGE Publishing 2018-03-01
Series:Journal of Low Frequency Noise, Vibration and Active Control
Online Access:https://doi.org/10.1177/1461348417752988
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author Sergio A Puga-Guzmán
Carlos Aguilar-Avelar
Javier Moreno-Valenzuela
Víctor Santibáñez
author_facet Sergio A Puga-Guzmán
Carlos Aguilar-Avelar
Javier Moreno-Valenzuela
Víctor Santibáñez
author_sort Sergio A Puga-Guzmán
collection DOAJ
description In this paper, the tracking control of periodic oscillations in an underactuated mechanical system is discussed. The proposed scheme is derived from the feedback linearization control technique and adaptive neural networks are used to estimate the unknown dynamics and to compensate uncertainties. The proposed neural network-based controller is applied to the Furuta pendulum, which is a nonlinear and nonminimum phase underactuated mechanical system with two degrees of freedom. The new neural network-based controller is experimentally compared with respect to its model-based version. Results indicated that the proposed neural algorithm performs better than the model-based controller, showing that the real-time adaptation of the neural network weights successfully estimates the unknown dynamics and compensates uncertainties in the experimental platform.
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spelling doaj.art-a55c0fee7c8b4629b94e62a14e1dd1e52022-12-22T01:05:48ZengSAGE PublishingJournal of Low Frequency Noise, Vibration and Active Control1461-34842048-40462018-03-013710.1177/1461348417752988Tracking of periodic oscillations in an underactuated system via adaptive neural networksSergio A Puga-GuzmánCarlos Aguilar-AvelarJavier Moreno-ValenzuelaVíctor SantibáñezIn this paper, the tracking control of periodic oscillations in an underactuated mechanical system is discussed. The proposed scheme is derived from the feedback linearization control technique and adaptive neural networks are used to estimate the unknown dynamics and to compensate uncertainties. The proposed neural network-based controller is applied to the Furuta pendulum, which is a nonlinear and nonminimum phase underactuated mechanical system with two degrees of freedom. The new neural network-based controller is experimentally compared with respect to its model-based version. Results indicated that the proposed neural algorithm performs better than the model-based controller, showing that the real-time adaptation of the neural network weights successfully estimates the unknown dynamics and compensates uncertainties in the experimental platform.https://doi.org/10.1177/1461348417752988
spellingShingle Sergio A Puga-Guzmán
Carlos Aguilar-Avelar
Javier Moreno-Valenzuela
Víctor Santibáñez
Tracking of periodic oscillations in an underactuated system via adaptive neural networks
Journal of Low Frequency Noise, Vibration and Active Control
title Tracking of periodic oscillations in an underactuated system via adaptive neural networks
title_full Tracking of periodic oscillations in an underactuated system via adaptive neural networks
title_fullStr Tracking of periodic oscillations in an underactuated system via adaptive neural networks
title_full_unstemmed Tracking of periodic oscillations in an underactuated system via adaptive neural networks
title_short Tracking of periodic oscillations in an underactuated system via adaptive neural networks
title_sort tracking of periodic oscillations in an underactuated system via adaptive neural networks
url https://doi.org/10.1177/1461348417752988
work_keys_str_mv AT sergioapugaguzman trackingofperiodicoscillationsinanunderactuatedsystemviaadaptiveneuralnetworks
AT carlosaguilaravelar trackingofperiodicoscillationsinanunderactuatedsystemviaadaptiveneuralnetworks
AT javiermorenovalenzuela trackingofperiodicoscillationsinanunderactuatedsystemviaadaptiveneuralnetworks
AT victorsantibanez trackingofperiodicoscillationsinanunderactuatedsystemviaadaptiveneuralnetworks