Identification of the inverse dynamics of robot manipulators with the structured kernel

Abstract

The inverse dynamics model of robots is often the key for accurate control. Especially in the computed torque control, the nonlinearity and the friction can be compensated, leading to better performance. The inverse models, however, is not trivial. The traditional Euler-Lagrange model based on the rigid body assumption often underfits in the presence of frictions and requires tedious derivations; the learning-based model needs larger training data set, since the structure of the dynamics is not considered. To overcome the aforementioned issues, we propose a structured kernel to replace the rigid body model and combine it with the universal radial basis kernel by direct sum. The proposed structured kernel asymptotically has the same convergence rate as the traditional model, and is general regardless of the configuration of the robot. Therefore, no analytic derivation is needed. Together with the universal radial basis kernel, the proposed approach enjoys the advantages of both the conventional and the learning-based models. To verify the proposed method, the simulations are used to investigate the performance in terms of the prediction errors.