2007 IEEE/RSJ International Conference on Intelligent Robots and Systems 2007
DOI: 10.1109/iros.2007.4399007
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An adaptive control for a free-floating space robot by using inverted chain approach

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Cited by 37 publications
(12 citation statements)
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“…In order for the controller to handle these changes, an adaptation law may be designed. Satoko and Hirzinger [187] proposed an adaptive controller for this purpose. They focused on the uncertainty of kinematic mapping, which included the dynamic parameters of the system.…”
Section: B Free-flying Casementioning
confidence: 99%
“…In order for the controller to handle these changes, an adaptation law may be designed. Satoko and Hirzinger [187] proposed an adaptive controller for this purpose. They focused on the uncertainty of kinematic mapping, which included the dynamic parameters of the system.…”
Section: B Free-flying Casementioning
confidence: 99%
“…This remains a topic for further investigation. It is noteworthy, however, that Abiko and Hirzinger [10] have implemented this style of adaptation in simulation for a free-floating space manipulator in which only the grasped payload, rather than the entire system, has uncertain properties. It stands to reason that a grasped payload might be the most uncertain part of the system in a great many applications of practical interest, and the possibility of using DYMAFLEX in this manner will be explored in future simulations.…”
Section: Possible Adaptation Schemesmentioning
confidence: 99%
“…Furthermore, the dynamic model of FSRMs cannot be linearly parameterized because of the free-floating base [10][11], which means the adaptive controllers for linearly parameterized models such as [12] are inapplicable. Hence, many research articles regarding motion control of FSRMs focus on handling nonlinear parameter uncertainties such as [13][14][15].…”
Section: Introductionmentioning
confidence: 99%
“…Many algorithms have been proposed to cope with external disturbances and unmodelled nonlinearities, including neural networks (NN) [16][17][18][19][20][21][22][23], fuzzy logic approximators (FLA) [7,[24][25][26][27] and adaptive disturbance observers (ADO) [28][29][30][31][32]. Such algorithms have a stronger capability for motion control of FSRMs over control schemes [13][14][15] that can only handle parameter uncertainties. More precisely, Jia and Shan [16] proposed a finite-time terminal sliding mode controller for space manipulators in which a radius basis function (RBF) neural network is used to compensate model uncertainties.…”
Section: Introductionmentioning
confidence: 99%