A dual neural network for kinematic control of redundant robot manipulators

The inverse kinematics problem in robotics can be formulated as a time-vary ing quadratic optimization problem. A new recurrent neural network, called the dual network, is presented in this paper. The proposed neural network i s composed of a single layer of neurons, and the number of neurons is equal to the dimensionality of the workspace. The proposed dual network is prove n to be globally exponentially stable. The proposed dual network is also sh own to be capable of asymptotic tracking for the motion control of kinemati cally redundant manipulators.