Machine Learning-Based Dynamic Modeling of Ball Joint Friction for Real-Time Applications

Lubricants, vol. 13, pp. 436

Abstract

Ball joints are components of the vehicle axle, and their friction characteristics must be considered when evaluating vibration behavior and ride comfort in driving simulator-based simulations. To model the three-dimensional friction behavior of ball joints, real-time capability and intuitive parameterization using data from standardized component test benches are essential. These requirements favor phenomenological modeling approaches. This paper applies a spherical, three-dimensional friction model based on the LuGre model, compares it with alternative approaches, and introduces a universal parameter estimation framework using machine learning. Furthermore, the kinematic operating ranges of ball joints are derived from vehicle measurements, and component-level measurements are conducted accordingly. The collected measurement data are used to estimate model parameters through gradient-based optimization for all considered models. The results of the model fitting are presented, and the model characteristics are discussed in the context of their suitability for online simulation in a driving simulator environment. We demonstrate that the proposed parameter estimation framework is capable of learning all the applied models. Moreover, the three-dimensional LuGre-based approach proves to be well suited for capturing the dynamic friction behavior of ball joints in real-time applications.

Authors 5

1. Kai Pfitzer corresponding

   BMW (Germany) · BMW Group (Germany) · Technische Universität Dresden

   Affiliation as printed

   BMW Group, Development Driving Experience, 80788 Munich, Germany

   Chair of Automobile Engineering, TU Dresden, 01069 Dresden, Germany

2. Lucas Rath Aachen Institute of Mechanism Theory

   BMW (Germany) · BMW Group (Germany) · RWTH Aachen University

   Affiliation as printed

   BMW Group, Development Driving Experience, 80788 Munich, Germany

   Institute of Mechanism Theory, Machine Dynamics and Robotics, RWTH Aachen, 52062 Aachen, Germany

3. Sebastian Kolmeder

   BMW (Germany) · BMW Group (Germany)

   Affiliation as printed

   BMW Group, Development Driving Experience, 80788 Munich, Germany

4. Burkhard J. Corves Aachen Institute of Mechanism Theory

   RWTH Aachen University

   Affiliation as printed

   Institute of Mechanism Theory, Machine Dynamics and Robotics, RWTH Aachen, 52062 Aachen, Germany

5. Günther Prokop corresponding

   Technische Universität Dresden

   Affiliation as printed

   Chair of Automobile Engineering, TU Dresden, 01069 Dresden, Germany