Two-stage adaptation of a position/force robot controller application of soft computing techniques

Friction of each joint of a robot manipulator has to be effectively compensated for in order to realize precise position/force control of robot manipulators. Recently, soft computing techniques (fuzzy reasoning, neural networks, and genetic algorithms) have played an important role in the control of robots. By applying soft computing techniques, learning/adaptation ability and human knowledge can be incorporated into a robot controller. In this paper, we propose a two-stage adaptive robot manipulator position/force control method in which uncertain/unknown dynamics of the environment are compensated for in the task space and joint friction is effectively compensated for in the joint space using soft computing techniques. The effectiveness of the proposed control method was evaluated by experiments.