Primal-dual neural network for joint torque optimization of redundant manipulators subject to torque limit constraints

Wai Sum Tang, Jun Wang

Research output: Contribution to journalConference articlepeer-review

Abstract

In this paper, a primal-dual neural network is proposed for the joint torque optimization of redundant manipulators subject to torque limit constraints. The neural network generates the minimum driving joint torques which never exceed the hardware limits and keep the end-effector to track a desired trajectory. The consideration of physical limits prevents the manipulator from torque saturation and hence ensuring a good tracking accuracy. The neural network is proven to be globally convergent to the optimal solution. The simulation results show that the neural network is capable of effectively computing the optimal redundancy resolution.

Original languageEnglish
Pages (from-to)IV-782 - IV-787
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume4
Publication statusPublished - 1999
Externally publishedYes
Event1999 IEEE International Conference on Systems, Man, and Cybernetics 'Human Communication and Cybernetics' - Tokyo, Jpn
Duration: 12 Oct 199915 Oct 1999

Fingerprint

Dive into the research topics of 'Primal-dual neural network for joint torque optimization of redundant manipulators subject to torque limit constraints'. Together they form a unique fingerprint.

Cite this