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Research Papers

Recursive Composite Adaptation for Robot Manipulators

[+] Author and Article Information
Hanlei Wang

Science and Technology on Space Intelligent Control Laboratory,
Beijing Institute of Control Engineering,
Beijing, 100190, China
e-mail: wanghanlei01@yahoo.com.cn

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received October 31, 2011; final manuscript received July 19, 2012; published online November 7, 2012. Assoc. Editor: Nariman Sepehri.

J. Dyn. Sys., Meas., Control 135(2), 021010 (Nov 07, 2012) (8 pages) Paper No: DS-11-1341; doi: 10.1115/1.4007557 History: Received October 31, 2011; Revised July 19, 2012

In this paper, we investigate the recursive implementation of composite adaptive control for robot manipulators. Via exploitation of the relation between the inertia matrix and the Coriolis and centrifugal matrix, we present the recursive algorithm for the derivation of the filtered manipulator model, which, to our knowledge, is the first result on this point in the literature. With this filtered model, the prediction error of the filtered torque is obtained and injected to the direct adaptation, forming the well-known composite adaptation law, with an acceptable amount of computation O(n2). A six degree-of-freedom (DOF) manipulator is employed as a simulation example to show the performance and the computational complexity of the proposed recursive algorithm.

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References

Figures

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Fig. 1

Link frames and geometrical parameters

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Fig. 2

A six-DOF manipulator grasping an unknown load

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Fig. 3

Position tracking errors (composite adaptation)

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Fig. 4

Parameter estimates (composite adaptation)

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Fig. 5

Position tracking errors (direct adaptation)

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Fig. 6

Parameter estimates (direct adaptation)

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Fig. 7

Position tracking errors (direct adaptation, practical case)

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Fig. 8

Parameter estimates (direct adaptation, practical case)

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Fig. 9

Position tracking errors (composite adaptation, practical case)

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Fig. 10

Parameter estimates (composite adaptation, practical case)

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