Neural Network Control by Learning the Inverse Dynamics of Uncertain Robotic Systems 불확실성이 있는 로봇 시스템의 역모델 학습에 의한 신경회로망 제어

This paper presents a study using neural networks in the design of the tracking controller of robotic systems. Our strategy is to put to use the available knowledge about the robot manipulator, such as estimation models, in the contoller design via the computed torque method, and then to add the neural network to control the remaining uncertainty. The neural network used here learns to provide the inverse dynamics of the plant uncertainty, and acts as an inverse controller. In the simulation study, we verify that the proposed neural network controller is robust not only to structured uncertainties, but also to unstructured uncertainties such as friction models.
Publisher
제어·로봇·시스템학회/대한전기학회
Issue Date
1995-12
Language
KOR
Citation

INTERNATIONAL JOURNAL OF CONTROL, AUTOMATION, AND SYSTEMS, v.1, no.2, pp.88 - 93

ISSN
1598-6446
URI
http://hdl.handle.net/10203/8365
Appears in Collection
EE-Journal Papers(저널논문)
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