TY - GEN A1 - Ortega, Romeo A1 - Bobtsov, Alexey A1 - Fang, Leyan A1 - Texis-Loaiza, Oscar A1 - Schiffer, Johannes T1 - Interturn fault detection in PMSMs : two adaptive observer-based noise insensitive solutions T2 - IEEE transactions on control systems technology N2 - In this article, we address the problem of online detection of interturn short-circuit faults (ITSCFs) that occur in interior- and surface-mounted permanent magnet synchronous motors (PMSMs). We propose two solutions to this problem: 1)a very simple linear observer and 2) a generalized parameter estimation-based observer, that incorporates a high performance estimator—with both observers detecting the short-circuit current and the fault intensity. Although the first solution guarantees the detection of the fault exponentially fast, the rate of convergence is fully determined by the motor parameters that, in some cases, may be too slow. The second observer, on the other hand, ensures finite convergence time (FCT) under the weakest assumption of interval excitation (IE). To make the observers adaptive, we develop a parameter estimator that, in the case of surface-mounted motors, estimates online (exponentially fast) the resistance and inductance of the motor. It should be underscored that, in contrast with existing observers (including the widely popular Kalman filter) that provide indirect information of the fault current, our observers provide an explicit one—namely the amplitude of the fault current. An additional advantage of the observers is that they do not require the knowledge of the motor currents, making them insensitive to current measurement noise. The performance of both observers, in their linear and generalized parameter estimation-based versions, is illustrated with realistic simulation studies. KW - Adaptive observer KW - Generalized parameter estimation-based observer (GPEBO) KW - Interturn short-circuit faults (ITSCFs) KW - Linear observer Y1 - 2025 U6 - https://doi.org/10.1109/TCST.2025.3612450 SN - 1063-6536 SN - 1558-0865 SP - 1 EP - 12 PB - IEEE CY - New York, NY ER - TY - GEN A1 - Hagemann, Willem A1 - Weichmann, Jaßper A1 - Gernandt, Hannes A1 - Krenzlin, Franziska A1 - Schiffer, Johannes T1 - Modeling and optimization of borehole thermal energy storage systems using physics-based neural networks T2 - Renewable energy : an international journal N2 - Borehole thermal energy storage (BTES) systems are critical components in the decarbonization of district heating networks as they enhance operational flexibility by seasonally storing thermal energy. While various modeling approaches for BTES exist, they are typically unsuitable for optimization problems and model-based control design. In this work, we propose a novel modeling approach that leads to a physics-based neural network surrogate model of the BTES temperature dynamics, capturing the fundamental dynamics with sufficient accuracy, while maintaining a relatively low complexity that makes it suitable for deployment in operational optimization or control algorithms. Specifically, we examine a standard BTES system that combines multiple heat sources, heat pumps, and storage buffers, detailing the associated mass flows and temperatures. We utilize a Python-based operational optimization process for the theoretical system setup using Pyomo and demonstrate that our modeling approach enables accurate optimizations over planning horizons of up to one year with a sample time of one hour. The new modeling approach significantly improves prediction accuracy across the relevant system states, with mean absolute errors reduced by approximately one-third compared to a single-capacitance model identified with the sparse identification framework SINDy. KW - Physics-based neural networks KW - Reduced order models KW - Borehole thermal energy storage KW - Operational optimization Y1 - 2026 U6 - https://doi.org/10.1016/j.renene.2025.123753 SN - 0960-1481 VL - 256, Part A SP - 1 EP - 14 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Archila, Oscar F. A1 - Wouwer, Alain Vande A1 - Schiffer, Johannes T1 - A multiple artificial potential functions approach for collision avoidance in UAV systems T2 - IEEE transactions on intelligent transportation systems N2 - Collision avoidance is a problem largely studied in robotics, particularly in uncrewed aerial vehicle (UAV) applications. The main challenges in this area are hardware limitations, the need for rapid response, and the uncertainty associated with obstacle detection. Artificial potential functions (APOFs) are a prominent method to address these challenges. However, existing solutions lack assurances regarding closed-loop stability and may result in chattering effects. Hence, we propose a high-level control method for static obstacle avoidance based on multiple artificial potential functions (MAPOFs), with a set of switching rules with conditions on the parameter tuning ensuring the stability of the final position. The stability proof is established by analyzing the closed-loop system using tools from hybrid systems theory. Furthermore, we validate the performance of the MAPOF control through simulations and real-life experiments, showcasing its effectiveness in avoiding static obstacles. KW - Artificial potential functions KW - Collision avoidance KW - Switched system KW - Uncrewed aerial vehicle (UAV) Y1 - 2025 U6 - https://doi.org/10.1109/TITS.2025.3579157 SN - 1524-9050 VL - 26 IS - 10 SP - 16688 EP - 16699 PB - IEEE CY - New York, NY ER - TY - GEN A1 - Texis-Loaiza, Oscar A1 - Mercado-Uribe, Angel A1 - Moreno, Jaime A. A1 - Schiffer, Johannes T1 - A finite-time convergent primal-dual gradient dynamics based on the multivariable super-twisting algorithm T2 - 2025 European Control Conference (ECC) N2 - We propose a novel primal-dual gradient dynamics (PDGD) algorithm to dynamically solve an optimization problem with linear equality constraints in finite time. To ensure finite-time convergence, we endow the PDGD with suitable homogeneity properties. More precisely, departing from the standard PDGD and based on the associated Lagrangian of the optimization problem, the algorithm is derived by suitably combining a change of coordinates of the standard PDGD with the multivariable super-twisting algorithm. In our new coordinates, the proposed PDGD’s global convergence to the optimal solution of the optimization problem is then proven via a smooth, strong Lyapunov function. Additionally, we provide a numerical example to compare the performance of our algorithm with existing approaches from the literature. Y1 - 2025 SN - 978-3-907144-12-1 U6 - https://doi.org/10.23919/ECC65951.2025.11186834 SP - 1892 EP - 1898 PB - IEEE CY - New York, NY ER - TY - GEN A1 - Texis-Loaiza, Oscar A1 - Moreno, Jaime A. A1 - Mercado-Uribe, Angel A1 - Schiffer, Johannes T1 - A third-order bl-homogeneous sliding mode observer for uncertain triangular nonlinear systems T2 - 2025 European Control Conference (ECC) N2 - In this paper, we propose a global third-order homogeneous in the bi-limit sliding mode observer (BL-H SMO) that can estimate the states of an uncertain nonlinear system with unknown inputs in finite time. The system must be strongly observable w.r.t. unknown inputs (UIs) and uniformly observable w.r.t. known inputs. To handle the UIs and non-Lipschitz nonlinearities, the proposed observer combines sliding-mode and high-gain observers, extending the generalized super-twisting observer. The convergence of the BL-H SMO to the system states is proven through a Lyapunov function. Finally, to showcase the effectiveness of the proposed method, the paper includes an academic example and a practical example, namely a three-phase converter with LCL-filter. Y1 - 2025 SN - 978-3-907144-12-1 U6 - https://doi.org/10.23919/ECC65951.2025.11186860 SP - 1955 EP - 1960 PB - IEEE CY - New York, NY ER - TY - GEN A1 - Cortés Martínez, Rolando A1 - Suryawanshi, Abhijeet Sanjay A1 - Ruffert, Christine A1 - Herglotz, Christian A1 - Schiffer, Johannes T1 - Towards a modular 5G motion capture testbed for indoor UAVs T2 - MikroSystemTechnik Kongress 2025 : Mikroelektronik, Mikrosystemtechnik und ihre Anwendungen - Nachhaltigkeit und Technologiesouveränität : proceedings : 27.-29. Oktober 2025, Duisburg Y1 - 2025 SN - 978-3-8007-6614-7 SN - 978-3-8007-6615-4 SP - 373ff. PB - VDE VERLAG GmbH CY - Berlin ER -