A Lagrangian network for kinematic control of redundant robot manipulators

Jun Wang, Qingni Hu, Danchi Jiang

Результат исследований: Вклад в журналСтатьярецензирование

89 Цитирования (Scopus)

Аннотация

A recurrent neural network, called the Lagrangian network, is presented for the kinematic control of redundant robot manipulators. The optimal redundancy resolution is determined by the Lagrangian network through real-time solution to the inverse kinematics problem formulated as a quadratic optimization problem. While the signal for a desired velocity of the end-effector is fed into the inputs of the Lagrangian network, it generates the joint velocity vector of the manipulator in its outputs along with the associated Lagrange multipliers. The proposed Lagrangian network is shown to be capable of asymptotic tracking for the motion control of kinematically redundant manipulators.

Язык оригиналаАнглийский
Страницы (с-по)1123-1132
Число страниц10
ЖурналIEEE Transactions on Neural Networks
Том10
Номер выпуска5
DOI
СостояниеОпубликовано - 1999
Опубликовано для внешнего пользованияДа

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