arXiv subjects

Hyuntae Kim

Publications and source records attributed to Hyuntae Kim.

9 recordsLinked to original sources

arXiv2026

Output-Feedback Integral Sliding-Mode Extremum Seeking with Super-Twisting Differentiation

This paper develops a dither-free output-feedback extremum-seeking controller for pre-stabilized nonlinear dynamic systems with unknown steady-state performance maps. The controller uses only the measured performance, the applied parameter, and internal states. Within a local state--parameter domain, integral sliding mode enforces equality between the applied parameter rate and a prescribed nonzero search velocity after finite-time capture under bounded matched rate-channel perturbations. This motion lets a super-twisting differentiator and rate normalization recover a gradient-sign surrogate without demodulation or averaging. A ramp-corrected moving-equilibrium coordinate cancels the first-order ramp-induced plant term and yields explicit flow, reversal, recovery, and dynamic-remainder bounds. The resulting local state--parameter tube supplies the regularity needed for finite-time exact differentiation. Combined with a deadbanded hybrid relay, these results establish local practical convergence to an explicit neighborhood of the unknown optimum. An 80-state tubular-reactor benchmark and paired held-out Monte Carlo tests compare a causal sampled realization with perturbation ESC and a rate-informed super-twisting comparator. The proposed realization succeeds in all 200 held-out cases and attains the smallest upper-tail objective gap under the stated tuning protocol. A matched-rate sweep connects performance degradation to applied-rate mismatch, near-stall motion, reversal, and regularization attenuation.

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arXiv2026

Finite Global $H_{\infty}$ Optimization of the $Q$-Filter Time Constant for Nominal-Response Recovery in SISO Disturbance Observers under Parametric Plant Uncertainty

This paper develops a finite real-algebraic procedure for global H-infinity optimization of the time constant of a fixed-shape Q-filter in a single-input/single-output (SISO) disturbance observer (DOB) under affine parametric plant uncertainty. Robust internal stability defines a possibly disconnected feasible domain, and the objective is the worst-case weighted mismatch between the uncertain DOB reference response and the nominal closed-loop response. A recovery-error factorization preserves affine dependence of the performance numerator and denominator on the same uncertainty vector. Phase lifting and a complex-polynomial Edge theorem reduce the uncertainty box to finitely many polynomial edges, while quadratic dependence of the squared frequency-domain inequality on the edge coordinate permits analytic elimination of that coordinate. The resulting semialgebraic formulation has only the squared frequency as a universally quantified variable. It determines the global infimum, decides whether it is attained, and constructs the complete optimizer set over the full robust-stability domain. A DOB case study with a plant family containing non-minimum-phase members yields a unique optimal time constant of approximately 0.0364075 and a worst-case norm of approximately 2.29975. The optimizer lies strictly inside the first robust-stability component, and every smaller feasible time constant in that component has a larger worst-case norm. Finite algebraic certificates establish robust stability, performance feasibility, and strict global optimality.

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arXiv2026

Exact Characterization of Admissible Bandwidths for Linear Disturbance Observers

This paper characterizes the entire set of admissible Q-filter bandwidths, equivalently parameterized by the time constant $τ$, that guarantee robust internal stability of a linear disturbance observer (DOB) under parametric plant uncertainties. Classical small-$τ$ stability results provide a rigorous foundation for Q-filter bandwidth design. Building on this foundation, we formulate the $τ$-dependent robust internal-stability problem as a semialgebraic feasibility problem in bivariate polynomials and prove that the complete admissible set is a finite union of open intervals under the standing assumptions. A constructive procedure with exact membership tests recovers all stabilizing intervals. A numerical example with an uncertain plant family containing non-minimum-phase members illustrates a disconnected admissible set.

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arXiv2026

Funnel-Precompensated Disturbance Observer for Uncertain Minimum-Phase SISO Linear Systems of Arbitrary Relative Degree

Disturbance observers (DOBs) recover nominal behavior, but fast filters risk peaking. We propose a funnel-precompensated DOB for uncertain minimum-phase single-input single-output linear systems of known relative degree. A stable-factor residual yields a minimum-phase channel. At relative degree two, gain conditions require positive plant high-frequency gains and one positive common design gain; for higher degrees, the common-design result also uses a plant-gain interval and mismatch condition. Global boundedness, practical nominal-output recovery, and open-funnel initialization are established.

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arXiv2026

Cross-Directional Modelling and Control of Slot-Die Battery Electrode Coating

As global battery demand increases, real-time process control becomes increasingly important for battery electrode manufacturing, yet slot-die lines are still mostly manually operated in open loop. This paper develops a physics-based modelling-and-control pipeline for film-thickness regulation. Computational fluid dynamics (CFD) simulations provide the data from which a low-order cross-directional model is identified and calibrated. Numerical simulations demonstrate close agreement between the CFD and the cross-directional model, which is used to design a controller that can be used in both real-time, automated feedback operation and manual feedforward operation during line commissioning.

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arXiv2024

Enhancing Anomaly Detection via Generating Diversified and Hard-to-distinguish Synthetic Anomalies

Unsupervised anomaly detection is a daunting task, as it relies solely on normality patterns from the training data to identify unseen anomalies during testing. Recent approaches have focused on leveraging domain-specific transformations or perturbations to generate synthetic anomalies from normal samples. The objective here is to acquire insights into normality patterns by learning to differentiate between normal samples and these crafted anomalies. However, these approaches often encounter limitations when domain-specific transformations are not well-specified such as in tabular data, or when it becomes trivial to distinguish between them. To address these issues, we introduce a novel domain-agnostic method that employs a set of conditional perturbators and a discriminator. The perturbators are trained to generate input-dependent perturbations, which are subsequently utilized to construct synthetic anomalies, and the discriminator is trained to distinguish normal samples from them. We ensure that the generated anomalies are both diverse and hard to distinguish through two key strategies: i) directing perturbations to be orthogonal to each other and ii) constraining perturbations to remain in proximity to normal samples. Throughout experiments on real-world datasets, we demonstrate the superiority of our method over state-of-the-art benchmarks, which is evident not only in image data but also in tabular data, where domain-specific transformation is not readily accessible. Additionally, we empirically confirm the adaptability of our method to semi-supervised settings, demonstrating its capacity to incorporate supervised signals to enhance anomaly detection performance even further.

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arXiv2023

Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller

This paper proposes a data-driven state feedback controller that enables reference tracking for nonlinear discrete-time systems. The controller is designed based on the identified inverse model of the system and a given reference model, assuming that the identification of the inverse model is carried out using only the system's state/input measurements. When its results are provided, we present conditions that guarantee a certain level of reference tracking performance, regardless of the identification method employed for the inverse model. Specifically, when Gaussian process regression (GPR) is used as the identification method, we propose sufficient conditions for the required data by applying some lemmas related to identification errors to the aforementioned conditions, ensuring that the Model reference-GPR (MR-GPR) controller can guarantee a certain level of reference tracking performance. Finally, an example is provided to demonstrate the effectiveness of the MR-GPR controller.

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arXiv2022

Model Reference Gaussian Process Regression: Data-Driven Output Feedback Controller

Data-driven controls using Gaussian process regression have recently gained much attention. In such approaches, system identification by Gaussian process regression is mostly followed by model-based controller designs. However, the outcomes of Gaussian process regression are often too complicated to apply conventional control designs, which makes the numerical design such as model predictive control employed in many cases. To overcome the restriction, our idea is to perform Gaussian process regression to the inverse of the plant with the same input/output data for the conventional regression. With the inverse, one can design a model reference controller without resorting to numerical control methods. This paper considers single-input single-output (SISO) discrete-time nonlinear systems of minimum phase with relative degree one. It is highlighted that the model reference Gaussian process regression controller is designed directly from pre-collected input/output data without system identification.

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arXiv2020

Prime knots with arc index 12 up to 16 crossings

As a continuation of the previous works to tabulate the prime knots up to arc index 11, we provide the list of prime knots with arc index 12 up to 16 crossings and their minimal grid diagrams. There are 19,513 prime knots of arc index 12 up to 16 crossings.

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