arXiv subjects

Kevin Schmidt

Publications and source records attributed to Kevin Schmidt.

8 recordsLinked to original sources

arXiv2026

Comparing Controller Synthesis Methods with Deadline-Miss Awareness

This paper presents a comparative study of real-time controller design methods for dynamical control systems subject to deadline overruns. Numerous techniques have been proposed to handle missed deadlines, i.e., including models or information of possible deadline misses directly in the control design. These approaches substantially differ in their assumptions, supported execution semantics, required runtime information, and provided guarantees, making direct comparison challenging. We address this gap with a qualitative assessment of representative deadline-miss-aware control methods. We analyze the design space of such controllers and identify the critical assumptions on the real-time constraints, the control design criteria and implementation aspects. We expose how these different assumptions lead to the different trade-offs in applicability, and control performance and guarantees. We contribute a comparison tool enabling researchers to systematically benchmark new techniques against the state of the art. The study is supported by simulations on two case studies: a Furuta pendulum and an electric motor to illustrate the practical behavior of the different approaches under missed-deadline conditions.

eess.SY↗

arXiv2026

Uncertainty Quantification Methods for Optimal Excitation Design in Parameter Identification

Parameter identification is crucial in virtual engineering processes, yet determining appropriate system excitations for identifying specific parameters remains challenging. In practice, extensive experimental programs often fail to generate data with sufficient information content for reliable parameter estimation. This work presents a systematic approach for deriving optimal excitations by maximizing the global sensitivity of target parameters across the space of possible excitation functions. To address the computational challenge of sensitivity evaluation during optimization, we develop two complementary approaches based on uncertainty quantification (UQ) methods. For systems with known mathematical structure, we present an intrusive polynomial chaos expansion (PCE) method that constructs deterministic surrogate models, enabling rapid sensitivity computation. For black-box models where intrusive approaches are not feasible, we introduce a novel non-intrusive method based on optimal transport theory, specifically using Wasserstein distances to quantify sensitivity measures without requiring knowledge of internal system dynamics. Both methods significantly reduce computational costs, making optimal excitation design practical for complex engineering systems. We demonstrate the effectiveness of both approaches on vehicle dynamics models, showing substantial improvements in parameter identification capability. The benefit for parameter identification is further validated experimentally on a test vehicle and compared to the state of the art.

math.OC↗

arXiv2026

Cooperative Adaptive Cruise Control with Variable Time Headway for Graceful Degradation under Fluctuating Network Quality of Service

This paper proposes a dynamic distance adaptation for Cooperative Adaptive Cruise Control (CACC) under time-varying network conditions. When the Quality of Service (QoS) drops below a level required to maintain desired inter-vehicle distances, an online adaptation of the reference distances, reflected by a change of the time headway factor, becomes necessary. We present a control design algorithm realizing a graceful degradation, for which a distance control to a virtual preceding vehicle is introduced. Furthermore, the Integral Quadratic Constraints (IQC) framework is applied to guarantee robust stability of the time-varying system. The concept is validated in simulation and experimentally using small-scale test vehicles.

eess.SY↗

arXiv2025

Sensemaking in Novel Environments: How Human Cognition Can Inform Artificial Agents

One of the most vital cognitive skills to possess is the ability to make sense of objects, events, and situations in the world. In the current paper, we offer an approach for creating artificially intelligent agents with the capacity for sensemaking in novel environments. Objectives: to present several key ideas: (1) a novel unified conceptual framework for sensemaking (which includes the existence of sign relations embedded within and across frames); (2) interaction among various content-addressable, distributed-knowledge structures via shared attributes (whose net response would represent a synthesized object, event, or situation serving as a sign for sensemaking in a novel environment). Findings: we suggest that attributes across memories can be shared and recombined in novel ways to create synthesized signs, which can denote certain outcomes in novel environments (i.e., sensemaking).

cs.AI↗

arXiv2023

Representational Tenets for Memory Athletics

We describe the current state of world-class memory competitions, including the methods used to prepare for and compete in memory competitions, based on the subjective report of World Memory Championship Grandmaster and co-author Nelson Dellis. We then explore the reported experiences through the lens of the Simulated, Situated, and Structurally coherent Qualia (S3Q) theory of consciousness, in order to propose a set of experiments to help further understand the boundaries of expert memory performance.

q-bio.NC↗

arXiv2022

Deep Learning-based Imaging in Radio Interferometry

The sparse layouts of radio interferometers result in an incomplete sampling of the sky in Fourier space which leads to artifacts in the reconstructed images. Cleaning these systematic effects is essential for the scientific use of radiointerferometric images. Established reconstruction methods are often time-consuming, require expert-knowledge, and suffer from a lack of reproducibility. We have developed a prototype Deep Learning-based method that generates reproducible images in an expedient fashion. To this end, we take advantage of the efficiency of Convolutional Neural Networks to reconstruct image data from incomplete information in Fourier space. The Neural Network architecture is inspired by super-resolution models that utilize residual blocks. Using simulated data of radio galaxies that are composed of Gaussian components we train Deep Learning models whose reconstruction capability is quantified using various measures. The reconstruction performance is evaluated on clean and noisy input data by comparing the resulting predictions with the true source images. We find that source angles and sizes are well reproduced, while the recovered fluxes show substantial scatter, albeit not worse than existing methods without fine-tuning. Finally, we propose more advanced approaches using Deep Learning that include uncertainty estimates and a concept to analyze larger images.

astro-ph.IM↗

arXiv2020

Initial Design of a W-band Superconducting Kinetic Inductance Qubit (Kineticon)

Superconducting qubits are widely used in quantum computing research and industry. We describe a superconducting kinetic inductance qubit (and introduce the term Kineticon to describe it) operating at W-band frequencies with a nonlinear nanowire section that provides the anharmonicity required for two distinct quantum energy states. Operating the qubits at higher frequencies may relax the dilution refrigerator temperature requirements for these devices and paves the path for multiplexing a large number of qubits. Millimeter-wave operation requires superconductors with relatively high $T_c$, which implies high gap frequency, 2$\Delta/h$, beyond which photons break Cooper pairs. For example, NbTiN with $T_c =15\,\text{K}$ has a gap frequency near 1.4 THz, which is much higher than that of aluminum (90 GHz), allowing for operation throughout the millimeter-wave band. Here we describe a design and simulation of a W-band Kineticon qubit embedded in a 3-D cavity. We perform classical electromagnetic calculations of the resulting field distributions.

quant-ph↗

arXiv2017

Yield Trajectory Tracking for Hyperbolic Age-Structured Population Systems

For population systems modeled by age-structured hyperbolic partial differential equations (PDEs) that are bilinear in the input and evolve with a positive-valued infinite-dimensional state, global stabilization of constant yield set points was achieved in prior work. Seasonal demands in biotechnological production processes give rise to time-varying yield references. For the proposed control objective aiming at a global attractivity of desired yield trajectories, multiple non-standard features have to be considered: a non-local boundary condition, a PDE state restricted to the positive orthant of the function space and arbitrary restrictive but physically meaningful input constraints. Moreover, we provide Control Lyapunov Functionals ensuring an exponentially fast attraction of adequate reference trajectories. To achieve this goal, we make use of the relation between first-order hyperbolic PDEs and integral delay equations leading to a decoupling of the input-dependent dynamics and the infinite-dimensional internal one. Furthermore, the dynamic control structure does not necessitate exact knowledge of the model parameters or online measurements of the age-profile. With a Galerkin-based numerical simulation scheme using the key ideas of the Karhunen-Lo\`eve-decomposition, we demonstrate the controller's performance.

math.OC↗