Modeling of an external force estimator for an end-effector of a robot by neural networks

This paper proposes a method to estimate external forces at the tip of a robot end-effector by using
a neural network model. In order to avoid the use of an expensive force sensor in the training purpose,
the proposed method implements the indirect training method by including the inverse dynamic model
of the robot manipulator to the training algorithm with available information from a default robot
system. In this method, the robot dynamics equations are necessary for the training, therefore
a disturbance observer is adopted to deal with the existing uncertainties and errors. The performance
of the proposed estimation method is evaluated through experiments of a 5-DOF robotic experimental
platform, comparing to another existing estimation method using recurrent neural network with a type-1
disturbance observer for the external force estimation. The estimation results show that the behavior of
the estimated external forces strongly correlates with the applied external forces and the proposed
method is superior to the other method.