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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| Published in | Asian journal of control Vol. 17; no. 2; pp. 636 - 647 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken
Blackwell Publishing Ltd
01.03.2015
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1561-8625 1934-6093 |
| DOI | 10.1002/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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| Bibliography: | ArticleID:ASJC912 istex:A11084607B88FBEDAD0E4B1C6B2A3092D3B45D89 ark:/67375/WNG-TNWQ7JFK-G ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 1561-8625 1934-6093 |
| DOI: | 10.1002/asjc.912 |