A neural network-based approach for variable admittance control in human-robot cooperation: online adjustment of the virtual inertia

This paper proposes an approach for variable admittance control in human-robot collaboration depending on the online training of neural network. The virtual inertia is an important factor for the system stability, and its tuning is investigated in improving the human-robot cooperation. The design of the variable virtual inertia controller is analyzed, and the choice of the neural network type and their inputs and output is justified. The error backpropagation analysis of the designed system is elaborated since the end-effector velocity error depends indirectly on the multilayer feedforward neural network output. The proposed controller performance is experimentally investigated, and its generalization ability is evaluated by conducting cooperative tasks with the help of multiple subjects using the KUKA LWR manipulator under different conditions and tasks than the ones used for the neural network training. Finally, a comparative study is presented between the proposed method and previous published ones.