Neural network-based kinematic inversion of industrial redundant robots using cooperative fuzzy hint for the joint limits avoidance

In this paper, a neural network (NN)-based inverse kinematics problem of redundant manipulators subject to joint limits is presented. The Widrow-Hoff NN with an adaptive learning algorithm derived by applying Lyapunov stability theory is introduced. Since the inverse kinematics has an 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 on the basis of cooperatively controlling each joint angle in the sense that it stops the motion on the critical axis at its limit at the expense of greater compensation from the most relaxed joint to accomplish the task. Physical constraints such as the joint velocity limits as well as the joint angle limits are incorporated into the method. Experiments are conducted for the PA-10 redundant manipulator to show the effectiveness of the proposed control system. A comparative study is carried out with the gradient projection method.