Two-stage adaptive robot position/force control using fuzzy reasoning and neural networks

Many studies have been performed on the position/force control of robot man ipulators. Since the desired position and force required to realize certain tasks are usually designated in the operational space, the controller shou ld adapt itself to an environment and generate the control force vector in the operational space. On the other hand, the friction of each joint of a r obot manipulator is a serious problem since it impedes control accuracy. Th erefore, the friction should be effectively compensated for in order to rea lize precise control of robot manipulators. Recently, soft computing techni ques (fuzzy reasoning, neural networks and genetic algorithms) have been pl aying an important role in the control of robots. Applying the fuzzy-neuro approach (a combination of fuzzy reasoning and neural networks), learning/a daptation ability and human knowledge can be incorporated into a robot cont roller. In this paper, we propose a two-stage adaptive robot manipulator po sition/force control method in which the uncertain/unknown dynamic of the e nvironment is compensated for in the task space and the joint friction is e ffectively compensated for in the joint space using soft computing techniqu es. The effectiveness of the proposed control method was evaluated by exper iments.