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

This paper presents an improved neural computation scheme for kinematic control of redundant manipulators based on infinity-norm joint velocity minimization. Compared with a preview 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