Learning friction estimation for sensorless force/position control in industrial manipulators

We present a novel type of friction estimation applied to the field of sensorless force/position control. As part of a position based neural force control (NFC-P) the estimation friction and external force allows a force/position control without using a force sensor. NFC-P consists of a hybrid force/position controller that accurately generates contact forces to objects with arbitrary flexibility and uncertain distance or shape. NFC-P performs force control by modifying the desired joint angle changes in force direction before they are fed into a computed torque controller. The inverse dynamics of the manipulator is modeled in a computed torque controller. Kinematic mappings guarantee singularity robustness in the entire workspace. Results from real time experiments are presented with a 6-DOF industrial manipulator as a testbed