End-Effector Force Estimation for Flexible-Joint Robots With Global Friction Approximation Using Neural Networks

Abstract

This paper proposes an improved disturbance observer to realize accurate contact force estimation using the joint torque sensor. The joint torque sensor separates the dynamics of the link side from the motor side of the robot manipulator. Therefore, only computing the partial dynamics on the link side can realize external force estimation. This can considerably reduce the modeling workload and error terms that may affect the estimation results. Furthermore, this paper presents that the observed residual value during free motion can be considered as the friction dynamics, which is approximated by the neural network (NN) due to its inherent capacity in approximating nonlinear functions. After that, the estimation accuracy of the observer is considerably improved. Compared to other local NN approximation method, we analyzes the properties of the friction force in detail to select appropriate excitation trajectory for accurate global approximation results using only limited training data. We have presented that the suitable excitation trajectory and the use of global basis function are the sufficient conditions for global friction approximation. The proof of this theorem is also given. The Kalman filter is also utilized to reduce the noise of the estimation results in real time. The experimental results also demonstrate the efficacy of the proposed method, which accurately estimates the contact force for flexible joint robots.