Variable Admittance Control for Human-Robot Collaboration based on Online Neural Network Training

In this paper, a method for variable admittance control in human-robot cooperation is proposed. A multilayer feedforward neural network is designed using the Cartesian velocity of the robot and the applied force by the operator as its inputs to modify online the virtual damping of the admittance controller. The neural network is trained online using the error backpropagation algorithm based on the error between the velocity of the minimum jerk trajectory model and the measured velocity of the robot. The performance of the proposed controller and the NN generalization ability are evaluated by conducting a point-to-point cooperative motion with multiple subjects using the KUKA LWR robot.