Fuzzy-neuro position/force control of robot manipulators-two-stage adaptation approach

Abstract

Position/force control is one of the most important and fundamental tasks of robot manipulators. Since the desired position and force required to perform certain tasks are usually designated in the operational space, the control force vector should be given to the end-effector in the operational space. However, friction of each joint of a robot manipulator impedes control accuracy. Therefore, friction should be effectively compensated for in order to realize precise control of robot manipulators. The fuzzy-neuro approach, a combination of fuzzy reasoning and neural networks, has been playing an important role in the control of robots. Applying the fuzzy-neuro approach, learning/adaptation ability and human knowledge can be incorporated into a robot controller. We propose an effective robot manipulator fuzzy-neuro position/force control method in which joint friction is effectively compensated for using adaptive friction models. The effectiveness of the proposed control method was evaluated by experiments.