Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/72.788651 | IEEE Transactions on Neural Networks |
Keywords | Field | DocType |
kine- matically redundant manipulators,joint velocity vector,kinematic control,motion control,quadratic optimization problem,proposed lagrangian network,neurocontrollers,asymptotic stability,inverse kinematics,recur- rent neural networks.,inverse kinematics problem,kinematically redundant manipulator,redundant robot manipulator,redundant manipulators,tracking,redundancy,optimization method,quadratic optimization,recurrent neural nets,recurrent neural network,lagrangian network,real-time systems,index terms— asymptotic stability,engineering,indexing terms,kinematics,manufacturing,nonlinear equations,lagrange multiplier,recurrent neural networks,real time systems,closed form solution,real time,robot control,neural network | Motion control,Kinematics,Inverse kinematics,Lagrange multiplier,Control theory,Computer science,Recurrent neural network,Redundancy (engineering),Quadratic programming,Lagrangian relaxation | Journal |
Volume | Issue | ISSN |
10 | 5 | 1045-9227 |
Citations | PageRank | References |
53 | 3.07 | 26 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Wang | 1 | 9228 | 736.82 |
| Q Hu | 2 | 60 | 3.50 |
| Danchi Jiang | 3 | 216 | 15.57 |