arXiv subjects

Benjamin Hartmann

Publications and source records attributed to Benjamin Hartmann.

4 recordsLinked to original sources

arXiv2026

Horizon Selection in Physics-Enhanced Neural ODEs: Theoretical Insights and Flux Linkage Application

The integration horizon during the training plays a critical role in Physics-Enhanced Neural Ordinary Differential Equations. We draw conclusions about horizon extension in the training of Neural Ordinary Differential Equations based on classical nonlinear system identification of input-output models. In light of this insight, we propose a framework that exploits longer horizons to reduce bias in physical parameter estimates, extracts residual information from data, and acts as a regularizer improving generalization. In the learning of a model for permanent magnet synchronous machine, the method is used to jointly estimate the flux map and the resistance.

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arXiv2026

Joint identification of permanent magnet synchronous machine and inverter

In electric drive modeling, identifying the magnetic flux maps is essential for predicting accurately the torque, parameterizing a controller for tracking the torque or creating a simulation model. However, the voltage output by the controller (commanded voltage) is usually disturbed by non-linearity of the inverter, which needs to be taken into account. This paper presents a novel approach to enhance the offline flux identification from commanded voltage inputs, circumventing the need for prior identification of inverter parameters. In the dq reference frame, the flux maps are represented as static relationships between dq fluxes and dq currents. We utilize a tensor product spline model to accurately capture saturation and cross-saturation effects. The effects of the voltage disturbance on the estimated flux maps are not negligible. It is demonstrated that a joint identification of the flux maps and a simple model of the inverter can highly improve the flux model. The method is validated on FEM simulation and test-bench data.

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arXiv2024

Feedforward Controllers from Learned Dynamic Local Model Networks with Application to Excavator Assistance Functions

Complicated first principles modelling and controller synthesis can be prohibitively slow and expensive for high-mix, low-volume products such as hydraulic excavators. Instead, in a data-driven approach, recorded trajectories from the real system can be used to train local model networks (LMNs), for which feedforward controllers are derived via feedback linearization. However, previous works required LMNs without zero dynamics for feedback linearization, which restricts the model structure and thus modelling capacity of LMNs. In this paper, we overcome this restriction by providing a criterion for when feedback linearization of LMNs with zero dynamics yields a valid controller. As a criterion we propose the bounded-input bounded-output stability of the resulting controller. In two additional contributions, we extend this approach to consider measured disturbance signals and multiple inputs and outputs. We illustrate the effectiveness of our contributions in a hydraulic excavator control application with hardware experiments. To this end, we train LMNs from recorded, noisy data and derive feedforward controllers used as part of a leveling assistance system on the excavator. In our experiments, incorporating disturbance signals and multiple inputs and outputs enhances tracking performance of the learned controller. A video of our experiments is available at https://youtu.be/lrrWBx2ASaE.

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arXiv2023

Transformation of social relationships in COVID-19 America: Remote communication may amplify political echo chambers

The COVID-19 pandemic, with millions of Americans compelled to stay home and work remotely, presented an opportunity to explore the dynamics of social relationships in a predominantly remote world. Using the 1972-2022 General Social Surveys, we found that the pandemic significantly disrupted the patterns of social gatherings with family, friends, and neighbors, but only momentarily. Drawing from the nationwide ego-network surveys of 41,033 Americans from 2020 to 2022, we found that the size and composition of core networks remained stable, though political homophily increased among non-kin relationships compared to previous surveys between 1985 and 2016. Critically, heightened remote communication during the initial phase of the pandemic was associated with increased interaction with the same partisans, though political homophily decreased during the later phase of the pandemic when in-person contacts increased. These results underscore the crucial role of social institutions and social gatherings in promoting spontaneous encounters with diverse political backgrounds.

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