arXiv subjects

Hyochoong Bang

Publications and source records attributed to Hyochoong Bang.

6 recordsLinked to original sources

arXiv2026

Chance-Constrained Nonlinear Covariance Control via Robust Linearization Remainder Bounds

When dealing with nonlinear systems, classical covariance steering typically propagates uncertainty via first-order linearizations, discarding higher-order Taylor remainders. This truncation causes computed statistical moments to diverge from the true physical state distribution, often leading to chance constraint violations. This paper introduces a discrete-time Sequential Convex Programming (SCP) framework that casts the deterministic one-step nonlinear numerical map as a Linear Stochastic Inclusion. The The Taylor remainder is modeled as a state-independent unstructured uncertainty block, bounded over a uniform envelope. The second-moment tubes are propagated via what we refer to as a robust Stochastic Linear Matrix Inequality (S-LMI) derived from the Petersen's lemma, providing an upper bound on the expected uncentered second moment. Domain-exit risk is bounded analytically via a Markov trace inequality, and spatial chance constraints are enforced via Gauss unimodal second-moment bounds within a Difference-of-Convex program. Simulations on a state-dependent nonlinear dynamic system demonstrate constraint satisfaction.

eess.SY↗

arXiv2026

Contours-Seeking Proposal Density Particle Filter and Resilient Terrain-Referenced Navigation

Auxiliary navigation systems are essential for the robust operation of aerial vehicles, particularly in self-contained frameworks like terrain-referenced navigation. However, challenges such as multimodal likelihoods, highly nonlinear terrain elevations, and unknown prediction biases result in highly multimodal and less predictable posterior distributions, leading to particle filter degeneration. This study addresses the numerical instability and degeneration of the particle filter approach by proposing a sampling strategy tailored to this problem. The approach introduces a Gaussian mixture random forcing mechanism, which nudges particles along terrain slopes and against biases towards the most probable terrain contours. Each mixture is associated with a mode of likelihood, enhancing adaptability to unmodeled terrain features. To further improve effectiveness, auxiliary sampling selectively applies this mixture sampling to probable particles, yielding a less degenerate and evenly weighted particle set. Numerical experiments demonstrate the effectiveness of the proposed method in reducing weight variance, improving effective sample size. In addition, the approach exhibits strong resilience under deteriorating scenarios, such as severe unknown prediction bias and multimodal measurement noise, ensuring long-term reliable particle filtering.

eess.SP↗

arXiv2025

Reinforcement Learning-based Fault-Tolerant Control for Quadrotor with Online Transformer Adaptation

Multirotors play a significant role in diverse field robotics applications but remain highly susceptible to actuator failures, leading to rapid instability and compromised mission reliability. While various fault-tolerant control (FTC) strategies using reinforcement learning (RL) have been widely explored, most previous approaches require prior knowledge of the multirotor model or struggle to adapt to new configurations. To address these limitations, we propose a novel hybrid RL-based FTC framework integrated with a transformer-based online adaptation module. Our framework leverages a transformer architecture to infer latent representations in real time, enabling adaptation to previously unseen system models without retraining. We evaluate our method in a PyBullet simulation under loss-of-effectiveness actuator faults, achieving a 95% success rate and a positional root mean square error (RMSE) of 0.129 m, outperforming existing adaptation methods with 86% success and an RMSE of 0.153 m. Further evaluations on quadrotors with varying configurations confirm the robustness of our framework across untrained dynamics. These results demonstrate the potential of our framework to enhance the adaptability and reliability of multirotors, enabling efficient fault management in dynamic and uncertain environments. Website is available at http://00dhkim.me/paper/rl-ftc

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arXiv2024

Sparse Identification of Nonlinear Dynamics-based Model Predictive Control for Multirotor Collision Avoidance

This paper proposes a data-driven model predictive control for multirotor collision avoidance considering uncertainty and an unknown model from a payload. To address this challenge, sparse identification of nonlinear dynamics (SINDy) is used to obtain the governing equation of the multirotor system. The SINDy can discover the equations of target systems with low data, assuming that few functions have the dominant characteristic of the system. Model predictive control (MPC) is utilized to obtain accurate trajectory tracking performance by considering state and control input constraints. To avoid a collision during operation, MPC optimization problem is again formulated using inequality constraints about an obstacle. In simulation, SINDy can discover a governing equation of multirotor system including mass parameter uncertainty and aerodynamic effects. In addition, the simulation results show that the proposed method has the capability to avoid an obstacle and track the desired trajectory accurately.

cs.RO↗

arXiv2024

Fast State-of-Health Estimation Method for Lithium-ion Battery using Sparse Identification of Nonlinear Dynamics

Lithium-ion batteries (LIBs) are utilized as a major energy source in various fields because of their high energy density and long lifespan. During repeated charging and discharging, the degradation of LIBs, which reduces their maximum power output and operating time, is a pivotal issue. This degradation can affect not only battery performance but also safety of the system. Therefore, it is essential to accurately estimate the state-of-health (SOH) of the battery in real time. To address this problem, we propose a fast SOH estimation method that utilizes the sparse model identification algorithm (SINDy) for nonlinear dynamics. SINDy can discover the governing equations of target systems with low data assuming that few functions have the dominant characteristic of the system. To decide the state of degradation model, correlation analysis is suggested. Using SINDy and correlation analysis, we can obtain the data-driven SOH model to improve the interpretability of the system. To validate the feasibility of the proposed method, the estimation performance of the SOH and the computation time are evaluated by comparing it with various machine learning algorithms.

cs.RO↗

arXiv2018

Multisensor Management Algorithm for Airborne Sensors Using Frank-Wolfe Method

This study proposes an airborne multisensor management algorithm for target tracking, taking each of multiple unmanned aircraft as a sensor. The purpose of the algorithm is to determine the configuration of the sensor deployment and to guide the mobile sensors to track moving targets in an optimal way. The cost function as a performance metric is defined as a combination of the D-optimality criterion of the Fisher information matrix. The convexity of the cost function is proved and the optimal solution for deployment and guidance problem is derived by the Frank-Wolfe method, also known as the conditional gradient descent method. An intuitive optimal approach to deal with the problem is to direct the sensor to the optimal position obtained by solving a nonlinear optimization problem. On the other hand, the proposed method takes the conditional gradient of the cost function as the command to the deployed sensors, so that the sensors are guaranteed to be in the feasible points and they achieve the current best performance. Simulation results demonstrate that the proposed algorithm provides better performance than directing each sensor to its optimal position.

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