## 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 language | English |
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Pages | 2005-2010 |

Number of pages | 6 |

Publication status | Published - 1999 |

Externally published | Yes |

Event | International Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA Duration: 10 Jul 1999 → 16 Jul 1999 |

### Conference

Conference | International Joint Conference on Neural Networks (IJCNN'99) |
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City | Washington, DC, USA |

Period | 10/07/99 → 16/07/99 |