Active Disturbance Rejection Control Applied To A Delta Parallel Robot In Trajectory Tracking Tasks

In this article, the problem of robust trajectory tracking, for a parallel robot is tackled via an observer‐based active disturbance rejection controller. The proposed design method is based on purely linear disturbance observation and linear feedback control techniques modulo nonlinear input gain i...

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Bibliographic Details
Published inAsian journal of control Vol. 17; no. 2; pp. 636 - 647
Main Authors Ramírez-Neria, Mario, Sira-Ramírez, Hebertt, Luviano-Juárez, Alberto, Rodrguez-Ángeles, Alejandro
Format Journal Article
LanguageEnglish
Published Hoboken Blackwell Publishing Ltd 01.03.2015
Wiley Subscription Services, Inc
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ISSN1561-8625
1934-6093
DOI10.1002/asjc.912

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Summary:In this article, the problem of robust trajectory tracking, for a parallel robot is tackled via an observer‐based active disturbance rejection controller. The proposed design method is based on purely linear disturbance observation and linear feedback control techniques modulo nonlinear input gain injections and cancellations. The estimations are carried out through Generalized Proportional Integral (GPI) observers, endowed with output integral injection to ease the presence of possible zero mean measurement noise effects. As the lumped (both exogenous and endogenous) disturbance inputs are estimated, they are being used in the linear controllers for on‐line disturbance cancellation, while the phase variables are being estimated by the same GPI observer. The estimations of the phase variables are used to complete a linear multivariable output feedback controller. The proposed control scheme does not need the exact knowledge of the system, which is a good alternative to classic control schemes such as computed torque method, reducing the computation time. The estimation and control method is approximate, ensuring small as desired reconstruction and tracking errors. The reported results, including laboratory experiments, are better than the results provided by the classical model‐based techniques, shown to be better when the system is subject to endogenous and exogenous uncertainties.
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ISSN:1561-8625
1934-6093
DOI:10.1002/asjc.912