arXiv subjects

Sven Reimann

Publications and source records attributed to Sven Reimann.

3 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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arXiv2025

Tunable Real-Time Safety Filters via Set-Based Control Barrier Functions

Safety filters for industrial constrained systems are required to combine certified constraint satisfaction, predictable online computation, and a transparent tuning interface. Existing set-based filters are based on a well-established control invariant set design that scales favorably with state and input constraints, but typically intervene only at the set boundary. Control barrier function (CBF)-based filters, by contrast, provide tunable intervention but require a scalar barrier construction. This paper proposes a set-based CBF safety filter that turns a convex control invariant set directly into a tunable barrier via its Minkowski functional. The resulting filter is formulated as a single-level quadratic program (QP) in which one class-$\mathcal{K}^e$ parameter sets the intervention aggressiveness. Explicit convex formulations are derived for polytopic, zonotopic, and MPC-based invariant sets. Under standard bounded-disturbance assumptions, the resulting safety filter guarantees constraint satisfaction and asymptotic recovery into the invariant set. For tight real-time budgets, a learning-based approximation enables online acceleration, while the formal safety guarantees remain tied to the exact formulation. The method is validated in numerical studies and on a permanent-magnet synchronous motor drive, where an explicit QP implementation evaluates within a 150 microseconds sampling window and has a worst-case execution time of 28.04 microseconds.

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