Efficient and Robust Modeling of Nonlinear Mechanical Systems

TL;DR

This paper introduces a new dynamic modeling approach for nonlinear mechanical systems that enhances robustness to noise and reduces computation time, applicable to automotive and robotic systems.

Contribution

A novel formulation and automatic modeling procedure for nonlinear mechanical systems that outperform Euler-Lagrange models in robustness and efficiency.

Findings

Superior robustness against measurement noise

Faster inverse dynamics computation

Applicable to automotive and robotic systems

Abstract

The development of efficient and robust dynamic models is fundamental in the field of systems and control engineering. In this paper, a new formulation for the dynamic model of nonlinear mechanical systems, that can be applied to different automotive and robotic case studies, is proposed, together with a modeling procedure allowing to automatically obtain the model formulation. Compared with the Euler-Lagrange formulation, the proposed model is shown to give superior performances in terms of robustness against measurement noise for systems exhibiting dependence on some external variables, as well as in terms of execution time when computing the inverse dynamics of the system.