Fuzzy-neuro Position/Force Control of Robot Manipulators with Uncertainties

In this paper, a new robust robot force tracking impedance control scheme that has the capability to track a specified desired force and to compensate for uncertainties in environment stiffness as well as in robot dynamic model is proposed. The uncertainties in robot dynamics are compensated by a radial basis function network (RBFN) controller, and a fuzzy tuning mechanism is developed to generate the impedance model which describes the relationship between force and position/velocity error. Simulation studies based on a two-DOF robot manipulator are carried out and the results show that highly robust position/force tracking can be achieved in the presence of large uncertainties