Fuzzy-neural-network based position/force hybrid control for multiple robot manipulators

This paper studies the position/force hybrid control problem for multiple robot manipulators (MRMS), where robots handle a common tool cooperatively. Since there exists closed chains in the physical structure, the position and velocity of each manipulator are strictly constrained by the common tool. Furthermore, dynamic uncertainties make the entire system more complicated and coupled. The kinematic and dynamic models are first built, and the control strategy is designed using the idea of position/force hybrid control. The position controller is mainly composed of a fuzzy-neural-network, which is used to compensate the nonlinear part including unknown dynamics, a coordinative control item is also introduced to reduce the mutual influence among the robots. The force controller consists of a feedforward term and a proportional control term. The stability of the closed-loop system is analyzed by Lyapunov theory. Simulations using the ADAMS and MATLAB software are carried out to verify the proposed control strategy.