Neural Force Control (NFC) Applied to Industrial Manipulators in Interaction with Moving Rigid Objects
نویسندگان
چکیده
We developed a novel concept of hybrid mixed force position control based on neu ral networks NFC Neural Force Control to signi cantly expand the range of manipulator applications NFC includes neural approaches for complex robotic mappings such as inverse dynamics and kinematics A Neural Dynamics Network NDN as the essential component of a computed torque controller performs a fast and adaptive computation of the inverse manip ulator model The kinematic mappings are represented by a Neural Kinematics Network NKN The features of NKN provide singularity robustness and the han dling of constraints in jointspace and Cartesian space self collisions To guarantee a tender impact while establishing contact between manipulator and surface a cascaded velocity controller CVC is added to the NFC approach Simulations for a DOF industrial ma nipulator have proved that the NFC concept is capable to manage various demanding tasks such as screw re moval and surface tracking with high accuracy
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Neural Force Position Control in Cartesian Space for a DOF Industrial Robot Concept and First Results
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