We developed a novel concept of hybrid force/position control based on neural networks (NFC) to significantly expand the range of manipulator applications. NFC includes neural approaches for complex robotic mappings such as inverse dynamics and kinematics. The neural dynamics network, as an essential component of the computed torque controller, performs a fast and adaptive computation of the inverse manipulator model. The kinematic mappings are represented by a neural kinematics network (NKN). The features of NKN provide singularity robustness and the handling of constraints in joint space and Cartesian space. To guarantee a tender impact while establishing contact between manipulator and surface, a cascaded velocity controller is added to the NFC approach. Simulations for a 6-DOF industrial manipulator have proved that the NFC concept is capable to manage various demanding tasks such as screw removal and surface tracking with high accuracy.
['Mark J. Dapper', 'R. Maass', 'Volker Zahn', 'Rolf Eckmiller']
Neural force control (NFC) applied to industrial manipulators in interaction with moving rigid objects