A new adaptive learning rule

Messner, William C.; Horowitz, Roberto; Kao, Wei‐Wen; Boals, M.

doi:10.1109/robot.1990.126223

Cited by 50 publications

(62 citation statements)

References 14 publications

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“…Their disadvantage is the need to repetitively learn each unique task-they are unable to apply the knowledge of "learned" parameters to any but the original task. While much of the early work in this area was heuristic, recent results directly address stability and robustness [2], [22], thus establishing some of these techniques as theoretically sound alternatives to model-based adaptive robot control.…”

Section: Imentioning

confidence: 99%

Comparative experiments with a new adaptive controller for robot arms

Whitcomb¹,

Koditschek²

Proceedings. 1991 IEEE International Conference on Robotics and Automation

101

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This paper presents a new model-based adaptive controller and proof of its global asymptotic stability with respect to the standard rigid-body model of robot-arm dynamics. Experimental data from a study of one new and several established globally asymptotically stable adaptive controllers on two very different robot arms 1) demonstrate the superior tracking performance afforded by the model-based algorithms over conventional PD control, 2) demonstrate and compare the superior performance of adaptive model-based algorithms over their nonadaptive counterparts, 3) reconcile several previous contrasting empirical studies, and 4) examine contexts that compromise their advantage. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. NOTE: At the time of publication, Daniel Koditschek was affiliated with the University of Michigan. Currently, he is a faculty member of the School of Engineering at the University of Pennsylvania.

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Section: Imentioning

confidence: 99%

Comparative experiments with a new adaptive controller for robot arms

Whitcomb¹,

Koditschek²

Proceedings. 1991 IEEE International Conference on Robotics and Automation

101

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“…In [50], an adaptive learning principle that is used for the purpose of identification and controlling the robot manipulators is proposed. The function estimate is produced through combining the product of a predefined kernel with an influence function approximate.…”

Section: Repetitive Learning Control and Its Variantsmentioning

confidence: 99%

On the Development of Learning Control for Robotic Manipulators

Zhang

Wei

2017

Robotics

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Learning control for robotic manipulators has been developed over the past decade and to the best of the authors' knowledge, it is still in its infant development stage; the authors believe that it will become one of the most promising directions in the control area in robotic manipulators. Learning control in robotic manipulators is mainly used to address the issue that the friction at the joints of robotic mechanisms and other uncertainties may exist in the dynamic models, which are very complex and may even be impossible to model mathematically. In this paper, the authors review and discuss the learning control in robotic manipulators and some issues in learning control for robotic manipulators are also illustrated. This review is able to give a general guideline for future research in learning control for robotic manipulators.

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“…The CTM model is utilized to determine the flow profile recorded in the vehicle detector station. The procedure has been extended from the repetitive control technique described in [7,8] .It is assumed that the density and ramp flow profile is 24 hour periodic, and the on-ramp and off-ramp flows are represented as a convolution of a kernel on a constant periodic ramp parameter vector.…”

Section: Ramp Flow Imputationmentioning

confidence: 99%