Hybrid Data-Model-Driven External Force Estimation for Manipulators via Generalized Momentum-Based Third-Order Observer*

Abstract

Accurate dynamic modeling and external force estimation are crucial for high-precision robot control and applications. However, model incompleteness and external disturbances inevitably lead to a residual between the actual joint torque and the torque calculated by the identified dynamic model. To address this, this paper proposes a hierarchical fusion framework. First, a multi-layer perceptron neural network (MLPNN) is employed to systematically compensate for these joint torque residuals. Subsequently, a generalized momentum-based third-order external force observer is designed to enhance the accuracy of estimating external forces acting on the manipulator. This approach retains the interpretability inherent in physics-based models while augmenting generalization capability through data-driven correction. The advantages of the third-order external force observer are substantiated via comparative analysis with first- and second-order observers on a Simulink simulation platform using a 2-DOF planar manipulator. Furthermore, the effectiveness of the proposed method was validated through a dragging experiment conducted on a 6-DOF manipulator without end-effector force/torque sensor, demonstrating its performance in practical applications.