Abstract
To enhance the accuracy of excavator external forces estimation, a data-physics hybrid-driven excavator external forces estimation method is proposed. MultiLayer Perceptron (MLP) model is applied for nonlinear component modeling and is used to compensate for Rigid Body Dynamics (RBD) models. Gaussian Process Regression (GPR) model is developed to estimate the external forces and uncertainties based on the joint angles, angular velocities, cylinder actuation forces, and actuation end velocities of the excavator. The external forces estimated by the GPR models are used as a new measurement in the Disturbance Kalman Filter (DKF), and the uncertainty is incorporated into the covariance of the process noise. The constructed GPR-DKF estimator is validated on a scaled-down excavator. The results demonstrate the robustness and effectiveness of the proposed method in estimating out-of-distribution samples.
Published Version
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