Hard contact surface tracking for industrial manipulators with (SR) position based force control

We present a novel control concept that solves a wide range of surface tracking tasks for manipulators with defined contact to (moving) rigid objects. The position based neural force control (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. These are fed into a computed torque controller, where the inverse dynamics of the manipulator is represented by neural networks. NFC-P includes a neural trajectory generating tool for smooth and kinematical valid contact trajectories with the possibility to adapt the trajectories to the unknown shape of the surface online. The kinematical mappings guarantee singularity robustness (SR) in the entire workspace. Results from real-time experiments are presented using a 6-DOF industrial manipulator as testbed