Posture coordination control of two-manipulator system using projection neural network

Abstract

Abstract Nowadays, there are many models/algorithms designed for redundant manipulator control. Nevertheless, two-manipulator system is usually more efficient than single manipulator. However, there are a few studies investigated for the two-manipulator system control. Specifically, the posture control of the two-manipulator system is investigated in this paper. By analyzing the kinematics and considering the control task, two posture control schemes are proposed for two manipulators, respectively. Then, a posture coordination control scheme is proposed in the form of standard quadratic programming (QP). The projection neural network (PNN) model is further developed to handle the posture coordination control scheme. In addition, a one-iteration discrete PNN (DPNN) model is proposed for convenient numerical algorithm development and digital hardware implementation. Finally, sufficient experiments based on the two-manipulator system verify the effectiveness and superiority of the posture coordination control scheme, PNN model and one-iteration DPNN model.