arXiv subjects

Hyeonyeong Jang

Publications and source records attributed to Hyeonyeong Jang.

3 recordsLinked to original sources

arXiv2026

A Note on Emergent Behavior in Multi-agent Systems Enabled by Neuro-spike Communication

In this note, we present a novel synchronization framework for heterogeneous multi-agent systems enabled by neuro-spike communication, which induces emergence. Unlike conventional synchronization strategies that require continuous transmission of full-state data packets, our approach utilizes a bio-inspired neuromorphic amplifier to achieve practical synchronization via intermittent, 1-bit Dirac delta pulses. The proposed method drastically improves communication efficiency in terms of bandwidth and energy by minimizing the information payload to a single bit, with intermittent and asynchronous communication. We provide a rigorous convergence analysis of the proposed method and validate the proposed scheme through numerical examples.

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arXiv2026

Steady-state response assignment for a given disturbance and reference: Sylvester equation rather than regulator equations

Conventionally, the concept of moment has been primarily employed in model order reduction to approximate system by matching the moment, which is merely the specific set of steady-state responses. In this paper, we propose a novel design framework that extends this concept from "moment matching" for approximation to "moment assignment" for the active control of steady-state. The key observation is that the closed-loop moment of an interconnected linear system can be decomposed into the open-loop moment and a term linearly parameterized by the moment of the compensator. Based on this observation, we provide necessary and sufficient conditions for the assignability of desired moment and a canonical form of the dynamic compensator, followed by constructive synthesis procedure of compensator. This covers both output regulation and closed-loop interpolation, and further suggests using only the Sylvester equation, rather than regulator equations.

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arXiv2026

Inverse Learning-Based Output Feedback Control of Nonlinear Systems with Verifiable Guarantees

In this paper, we present a data-driven output feedback controller for nonlinear systems that achieves practical output regulation, using noise-free input/output measurement data. The proposed controller is based on (i) an inverse model of the system identified via kernel interpolation, which maps a desired output and the current state to the corresponding desired control input; and (ii) a data-driven reference selection framework that actively chooses a suitable desired output from the dataset which has been used for the identification. We establish a verifiable sufficient condition on the dataset under which the proposed controller guarantees practical output regulation. Numerical simulations demonstrate the effectiveness of the proposed controller, with additional evaluations in the presence of output measurement noise to assess its robustness empirically.

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