arXiv subjects

Se Young Yoon

Publications and source records attributed to Se Young Yoon.

4 recordsLinked to original sources

arXiv2026

Feedback Linearization and Control of a Grid-Forming Power Converter in an Islanded Microgrid

In an islanded setting, grid-forming inverters must regulate their terminal voltage without support from an external grid, even though the load current depends directly on that voltage. The usual approach is a cascaded proportional--integral (PI) controller, built on a fast inner current loop and a slower outer voltage loop, with feedforward terms used to compensate dq rotational coupling. However, this compensation is only exact at the operating point where the controller is tuned. This tutorial presents an alternative based on full-state feedback linearization. It is shown that the islanded inverter model has full relative degree, which allows exact state-space linearization with no internal or zero dynamics. A single feedback law cancels the main nonlinear effects; rotational coupling, resistive drops, and load conductance, so that the closed-loop system behaves like two independent double integrators. A standard pole-placement design is then used to shape the response. The controller is tested in MATLAB against a cascaded PI baseline under identical conditions at a 20 MW operating point, including reference tracking, load step disturbances, and parameter mismatch. The feedback-linearizing controller settles a reference step in 0.76 ms, while the PI controller does not reach the 2 % band within 50 ms. The cascaded PI controller shows better robustness to filter parameter mismatch due to its inner-loop integral action, which reduces steady-state errors under modeling uncertainty. Overall, the performance improvement and the robustness trade-off both come directly from the controller structures, rather than from tuning choices.

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arXiv2026

Optimal Derivative Feedback Control for an Active Magnetic Levitation System: An Experimental Study on Data-Driven Approaches

This paper presents the design and implementation of data-driven optimal derivative feedback controllers for an active magnetic levitation system. A direct, model-free control design method based on the reinforcement learning framework is compared with an indirect optimal control design derived from a numerically identified mathematical model of the system. For the direct model-free approach, a policy iteration procedure is proposed, which adds an iteration layer called the epoch loop to gather multiple sets of process data, providing a more diverse dataset and helping reduce learning biases. This direct control design method is evaluated against a comparable optimal control solution designed from a plant model obtained through the combined Dynamic Mode Decomposition with Control (DMDc) and Prediction Error Minimization (PEM) system identification. Results show that while both controllers can stabilize and improve the performance of the magnetic levitation system when compared to controllers designed from a nominal model, the direct model-free approach consistently outperforms the indirect solution when multiple epochs are allowed. The iterative refinement of the optimal control law over the epoch loop provides the direct approach a clear advantage over the indirect method, which relies on a single set of system data to determine the identified model and control.

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arXiv2025

Probabilistic Safety Guarantee for Stochastic Control Systems Using Average Reward MDPs

Safety in stochastic control systems, which are subject to random noise with a known probability distribution, aims to compute policies that satisfy predefined operational constraints with high confidence throughout the uncertain evolution of the state variables. The unpredictable evolution of state variables poses a significant challenge for meeting predefined constraints using various control methods. To address this, we present a new algorithm that computes safe policies to determine the safety level across a finite state set. This algorithm reduces the safety objective to the standard average reward Markov Decision Process (MDP) objective. This reduction enables us to use standard techniques, such as linear programs, to compute and analyze safe policies. We validate the proposed method numerically on the Double Integrator and the Inverted Pendulum systems. Results indicate that the average-reward MDPs solution is more comprehensive, converges faster, and offers higher quality compared to the minimum discounted-reward solution.

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arXiv2022

Output Regulation of Linear Aperiodic Sampled-Data Systems

This paper deals with the output regulation problem of a linear time-invariant system in the presence of sporadically available measurement streams. A regulator with a continuous intersample injection term is proposed, where the intersample injection is provided by a linear dynamical system and the state of which is reset with the arrival of every new measurement updates. The resulting system is augmented with a timer triggering an instantaneous update of the new measurement and the overall system is then analyzed in a hybrid system framework. With the Lyapunov based stability analysis, we offer sufficient conditions to ensure the objectives of the output regulation problem are achieved under intermittency of the measurement streams. Then, from the solution to linear matrix inequalities, a numerically tractable regulator design procedure is presented. Finally, with the help of an illustrative example, the effectiveness of the theoretical results are validated.

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