Efficient and Robust Modeling of Nonlinear Mechanical Systems

2026-02-06

AI Summary

This work proposes a novel nonlinear dynamics modeling framework that addresses the limitations of traditional approaches—such as Euler–Lagrange equations—which are highly susceptible to measurement noise and computationally inefficient when handling nonlinear mechanical systems with external variables. By integrating a noise-robust, computationally efficient model architecture with an automated modeling pipeline, the proposed method significantly enhances inverse dynamics prediction performance. Extensive validation in representative applications, including automotive and robotic systems, demonstrates clear advantages over conventional techniques in both noise resilience and computational speed, effectively overcoming the longstanding bottleneck in modeling complex systems with strong external dependencies.

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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.

Problem

• nonlinear mechanical systems
• dynamic modeling
• robustness
• measurement noise
• inverse dynamics

Innovation

• nonlinear mechanical systems
• robust modeling
• inverse dynamics
• automatic modeling procedure
• measurement noise robustness