Cooperative fuzzy hint acquisition for avoiding joint limits of redundant manipulators

In this paper, a back propagation neural network (NN) is presented for the inverse kinematics of redundant manipulator with joint limits. Since the inverse kinematics has infinite number of joint angle vectors, a fuzzy neural network (FNN) is designed to provide an approximate value for that vector. This vector is fed into the NN as a hint input vector to guide the output of the NN within the self-motion. This FNN is designed based on cooperatively controlled each joint angle in the sense that it stops the motion on the critical axis at its limit in the expense of more compensation from the most relaxed joint to accomplish the task. The joint velocity limits as well as the joint limits are incorporated in this method. Simulations are implemented based on four-link redundant manipulator to show the effectiveness of the proposed control system.