We present a new design of hybrid force/position control NVFC capable of friction-compensation, which is a mandatory requirement in industrial applications for precise tracking of hard surfaces with desired contact force. The control architecture consists of an outer-loop velocity controller and an inner-loop adaptive hybrid force/position controller, which compensates the friction of the manipulators joints, significantly improving the force tracking performance during slow movements. The cascaded velocity controller ensures the precise approach during neural force/position control with a reduced bounce into the unknown surface, which is a demanding requirement for tasks like deburring or chamfering. The resulting performance of the force control system is illustrated in simulations, and is consistent with velocity/force control experiments on a 6-DOF industrial manipulator controlled with a PC-based real-time control system.

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.

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.

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.