Improved neurocomputation scheme for minimum infinity-norm kinematic control of redundant manipulators

Wai Sum Tang, Jun Wang

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)

Abstract

This paper presents an improved neural computation scheme for kinematic control of redundant manipulators based on infinity-norm joint velocity minimization. Compared with a previous neural network approach to minimum infinity-norm kinematic control, the presented approach has a less complex architecture. The recurrent neural network explicitly minimizes the maximum component of the joint velocity vector while tracking a desired end-effector trajectory. The end-effector velocity vector for a given task is fed into the neural network from its input and the minimum infinity-norm joint velocity vector is generated at its output instantaneously. Analytical results are given to substantiate the asymptotic stability of the recurrent neural network. The simulation results of a four degree-of-freedom planar robot arm are presented to show the proposed neural network can effectively compute the minimum infinity-norm solution to redundant manipulators in real-time.

Original languageEnglish
Pages2005-2010
Number of pages6
Publication statusPublished - 1999
Externally publishedYes
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: 10 Jul 199916 Jul 1999

Conference

ConferenceInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA
Period10/07/9916/07/99

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