arXiv subjects

Huan Yu

Publications and source records attributed to Huan Yu.

At least 55 records · Page 3Linked to original sources

arXiv2025

Kinetic Simulations of Particle Acceleration in Relativistic Perpendicular Electron-positron Shocks with Proton Admixture

Particle acceleration in relativistic shocks of electron-positron plasmas with proton admixture is investigated through two-dimensional (2D) particle-in-cell (PIC) simulations. The upstream plasma, with a bulk Lorentz factor of $10$ and a magnetization parameter of 0.02, includes a small fraction of protons ($\sim 5\%$ by number). A relativistic perpendicular shock is formed by reflecting the flow off a conducting wall. The shock structure, electromagnetic fields, and particle energy spectra are analyzed. The particle density and the magnetic field have fluctuations. In the far-downstream region of the shock, positrons are accelerated to energies comparable to protons and develop a hard nonthermal component with a spectral index of $\sim 2$ in their energy spectrum, while electrons remain confined to lower energies. This asymmetry is attributed to the polarization properties of proton-driven electromagnetic waves, which favor positron acceleration. The results highlight the importance of plasma composition in shaping particle acceleration and nonthermal emission in relativistic shocks. These findings provide new insights into the microphysics of particle acceleration in astrophysical sources containing relativistic shocks.

astro-ph.HE↗

arXiv2025

Particle Acceleration along Magnetic Fields as the Origin of Ear-like Structures in Supernova Remnants

The origin of ear-like structures with two opposite lobes extending from the shell of supernova remnants (SNRs) remains a subject of active debate, with proposed mechanisms ranging from jet-driven explosions to interactions with bipolar circumstellar media. We present a novel investigation using Particle-in-Cell (PIC) simulations to examine the dynamics of a spherical ejecta in magnetized media, revealing a different formation mechanism for these structures. Our results demonstrate that particle acceleration is significantly enhanced along the magnetic-field direction, producing substantial modifications to shock morphology. These effects naturally generate elongated protrusions bearing a remarkable similarity to the observed SNR ear structures. This magnetic alignment scenario represents a distinct mechanism from the existing jet-based and circumstellar interaction models. Our findings suggest that magnetic field orientation may serve as a crucial diagnostic for distinguishing between different ear formation mechanisms in SNRs.

astro-ph.HE↗

arXiv2025

Neural Operators for Adaptive Control of Freeway Traffic

Uncertainty and delayed reactions in human driving behavior lead to stop-and-go traffic congestion on freeways. The freeway traffic dynamics are governed by the Aw-Rascle-Zhang (ARZ) traffic Partial Differential Equation (PDE) models with unknown relaxation time. Motivated by the adaptive traffic control problem, this paper presents a neural operator (NO) based adaptive boundary control design for the coupled 2$\times$2 hyperbolic systems with uncertain spatially varying in-domain coefficients and boundary parameter. In traditional adaptive control for PDEs, solving backstepping kernel online is computationally intensive, as it requires significant resources at each time step to update the estimation of coefficients. To address this challenge, we use operator learning, i.e. DeepONet, to learn the mapping from system parameters to the kernels functions. DeepONet, a class of deep neural networks designed for approximating operators, has shown strong potential for approximating PDE backstepping designs in recent studies. Unlike previous works that focus on approximating single kernel equation associated with the scalar PDE system, we extend this framework to approximate PDE kernels for a class of the first-order coupled 2$\times$2 hyperbolic kernel equations. Our approach demonstrates that DeepONet is nearly two orders of magnitude faster than traditional PDE solvers for generating kernel functions, while maintaining a loss on the order of $10^{-3}$.

math.OC↗

arXiv2025

PerchMobi^3: A Multi-Modal Robot with Power-Reuse Quad-Fan Mechanism for Air-Ground-Wall Locomotion

Achieving seamless integration of aerial flight, ground driving, and wall climbing within a single robotic platform remains a major challenge, as existing designs often rely on additional adhesion actuators that increase complexity, reduce efficiency, and compromise reliability. To address these limitations, we present PerchMobi^3, a quad-fan, negative-pressure, air-ground-wall robot that implements a propulsion-adhesion power-reuse mechanism. By repurposing four ducted fans to simultaneously provide aerial thrust and negative-pressure adhesion, and integrating them with four actively driven wheels, PerchMobi^3 eliminates dedicated pumps while maintaining a lightweight and compact design. To the best of our knowledge, this is the first quad-fan prototype to demonstrate functional power reuse for multi-modal locomotion. A modeling and control framework enables coordinated operation across ground, wall, and aerial domains with fan-assisted transitions. The feasibility of the design is validated through a comprehensive set of experiments covering ground driving, payload-assisted wall climbing, aerial flight, and cross-mode transitions, demonstrating robust adaptability across locomotion scenarios. These results highlight the potential of PerchMobi^3 as a novel design paradigm for multi-modal robotic mobility, paving the way for future extensions toward autonomous and application-oriented deployment.

cs.RO↗

arXiv2025

From Micro to Macro Flow Modeling: Characterizing Heterogeneity of Mixed-Autonomy Traffic

Most autonomous-vehicles (AVs) driving strategies are designed and analyzed at the vehicle level, yet their aggregate impact on macroscopic traffic flow is still not understood, particularly the flow heterogeneity that emerges when AVs interact with human-driven vehicles (HVs). Existing validation techniques for macroscopic flow models rely on high-resolution spatiotemporal data spanning entire road segments which are rarely available for mixed-autonomy traffic. AVs record detailed Lagrangian trajectories of the ego vehicle and surrounding traffic through onboard sensors. Leveraging these Lagrangian observations to validate mixed-autonomy flow models therefore remains an open research challenge. This paper closes the gap between microscopic Lagrangian data and macroscopic Euclidean traffic models by introducing a continuous traffic-heterogeneity attribute. We represent traffic flow with two coupled conservation laws with one for vehicle number and one for the traffic attribute. Reconstruction methods are designed to derive the traffic attribute from Lagrangian vehicle trajectories. When abundant trajectory data are available, we characterize traffic heterogeneity by extracting drivers' desired speed and local behavioral uncertainty from trajectories. In data-scarce mixed traffic, we design an end-to-end mapping that infers the traffic heterogeneity solely from trajectories in the current spatiotemporal region. Experiments across multiple traffic datasets show that the proposed model effectively captures traffic heterogeneity by clustering the fundamental diagram scatter into attribute-based groups. The calibration errors of traffic flow dynamics are also reduce by 20% relative to the Aw-Rascle-Zhang model benchmark. Detailed analyses further show that the model generalizes well, maintaining nearly the same accuracy when evaluated under a variety of previously unseen traffic conditions.

eess.SY↗

arXiv2025

The impact of the following vehicles behaviors on the car following behaviors of the ego-vehicle

Among all types of crashes, rear-end crashes dominate, which are closely related to the car-following (CF) behaviors. Traditional CF behavior models focused on the influence of the vehicle in front, but usually ignored the peer pressure from the surrounding road users, including the following vehicle (FV). Based on an open dataset, the highD dataset, we investigated whether the FV's states can affect the CF behavior of the ego-vehicle in CF events. Two types of CF events were extracted from highD database, including the tailgated events, where the time headway between the FV and the ego-vehicle (i.e., time gap) was smaller than 1 second, and the gapped events, where the time gap was larger than 3 seconds. The dynamic time warping was used to extract CF pairs with similar speed profiles of the leading vehicle (LV). Statistical analyses were conducted to compare the CF-performance metrics in tailgated and gapped events. Then, the inverse reinforcement learning was used to recover the reward function of the ego-vehicle drivers in different CF events. The results showed that the ego-driver would adjust their CF behavior in response to the pressure from a tailgating FV, by maintaining a closer distance to the LV, but at the same time, driving more cautiously. Further, drivers were still able to adjust their CF strategies based on the speed of traffic flow and the distance to the LV, even when being tailgated. These findings provide insights regarding more accurate modelling of traffic flow by considering the peer pressure from surrounding road users.

eess.SY↗

arXiv2025

Physics-Informed Neural Networks for Nonlocal Flow Modeling of Connected Automated Vehicles

Connected automated vehicles (CAVs) cruising control strategies have been extensively studied at the microscopic level. CAV controllers sense and react to traffic both upstream and downstream, yet most macroscopic models still assume locality, where the desired speed only depends on local density. The nonlocal macroscopic traffic flow models that explicitly capture the ``look ahead'' and ``look behind'' nonlocal CAV dynamics remain underexplored. In this paper, we propose a Physics-informed Neural Network framework to directly learn a macroscopic non-local flow model from a generic looking-ahead looking-behind vehicle motion model, which bridges the micro-macro modeling gap. We reconstruct macroscopic traffic states from synthetic CAV trajectories generated by the proposed microscopic control designs, and then learn a non-local traffic flow model that embeds a non-local conservation law to capture the resulting look-ahead look-behind dynamics. To analyze how CAV control parameters affect nonlocal traffic flow, we conduct high-fidelity driving simulator experiments to collect human drivers' trajectory data with varying downstream and upstream visibility, which serves as a baseline for tuning CAV control gains. Our analysis validates that the learned non-local flow model predicts CAV traffic dynamics more accurately than local models, and the fundamental diagram exhibits far less scatter in the speed - density relation. We further show that the looking-ahead/looking-behind control gains mainly reshape the non-local kernels, while the macroscopic speed and non-local density relation mainly depends on the desired speed function choice of the CAV controller. Our results provide a systematic approach for learning non-local macroscopic traffic-flow models directly from generic CAV control designs.

eess.SY↗

arXiv2025

Tailless Flapping-Wing Robot With Bio-Inspired Elastic Passive Legs for Multi-Modal Locomotion

Flapping-wing robots offer significant versatility; however, achieving efficient multi-modal locomotion remains challenging. This paper presents the design, modeling, and experimentation of a novel tailless flapping-wing robot with three independently actuated pairs of wings. Inspired by the leg morphology of juvenile water striders, the robot incorporates bio-inspired elastic passive legs that convert flapping-induced vibrations into directional ground movement, enabling locomotion without additional actuators. This vibration-driven mechanism facilitates lightweight, mechanically simplified multi-modal mobility. An SE(3)-based controller coordinates flight and mode transitions with minimal actuation. To validate the robot's feasibility, a functional prototype was developed, and experiments were conducted to evaluate its flight, ground locomotion, and mode-switching capabilities. Results show satisfactory performance under constrained actuation, highlighting the potential of multi-modal flapping-wing designs for future aerial-ground robotic applications. These findings provide a foundation for future studies on frequency-based terrestrial control and passive yaw stabilization in hybrid locomotion systems.

cs.RO↗

arXiv2025

Adaptive Event-triggered Formation Control of Autonomous Vehicles

This paper presents adaptive event-triggered formation control strategies for autonomous vehicles (AVs) subject to longitudinal and lateral motion uncertainties. The proposed framework explores various vehicular formations to enable safe and efficient navigation in complex traffic scenarios, such as narrow passages, collaborative obstacle avoidance, and adaptation to cut-in maneuvers. In contrast to conventional platoon control strategies that rely on predefined communication topologies and continuous state transmission, our approach employs a sampling-based observer to reconstruct vehicle dynamics. Building upon an adaptive backstepping continuous-time controller, we design three distinct event-triggered mechanisms, each offering a different trade-off between formation tracking performance and control efficiency by reducing the frequency of control signal updates. A Lyapunov-based stability analysis is conducted to guarantee bounded tracking errors and to avoid Zeno behavior. Finally, the proposed event-triggered controllers are validated through simulations of vehicular formation in three scenarios, highlighting their impact on traffic safety and mobility.

eess.SY↗

arXiv2025

Neural Operators for Adaptive Control of Traffic Flow Models

The uncertainty in human driving behaviors leads to stop-and-go instabilities in freeway traffic. The traffic dynamics are typically modeled by the Aw-Rascle-Zhang (ARZ) Partial Differential Equation (PDE) models, in which the relaxation time parameter is usually unknown or hard to calibrate. This paper proposes an adaptive boundary control design based on neural operators (NO) for the ARZ PDE systems. In adaptive control, solving the backstepping kernel PDEs online requires significant computational resources at each timestep to update estimates of the unknown system parameters. To address this, we employ DeepONet to efficiently map model parameters to kernel functions. Simulations show that DeepONet generates kernel solutions nearly two orders of magnitude faster than traditional solvers while maintaining a loss on the order of $10^{-2}$. Lyapunov analysis further validates the stability of the system when using DeepONet-approximated kernels in the adaptive controller. This result suggests that neural operators can significantly accelerate the acquisition of adaptive controllers for traffic control.

math.OC↗

arXiv2025

Mild ill-posedness in $W^{1,\infty}$ for the incompressible porous media equation

In this paper, we establish the mild ill-posedness of 2D IPM equation in the critical Sobolev space $W^{1,\infty}$ when the initial data are small perturbations of stable profile $g(x_2).$ Consequently, instability can be inferred. Notably, our results are valid for arbitrary vertically stratified density profiles $g(x_2)$ without imposing any restrictions on the sign of $g'(x_2).$ From a physical perspective, since gravity acts downward, density profiles satisfying $g'(x_2) < 0$ typically correspond to stable configurations, whereas those with $g '(x_2) > 0$ are generally expected to be unstable. Surprisingly, our analysis uncovers an unexpected instability even when $g'(x_2) < 0$ and $g'(x_2)\in W^{2,\infty}(\mathbb{R})$. To the best of our knowledge, this work provides the first rigorous demonstration of IPM instability for vertically nonlinear density profiles, marking a significant departure from conventional physical expectations.

math.AP↗

arXiv2024

CapsuleBot: A Novel Hybrid Aerial-Ground Bi-Copter Robot With Two Actuated-Wheel-Rotors

This paper presents the design, modeling, and experimental validation of CapsuleBot, a novel hybrid aerial-ground bi-copter robot designed for long-endurance and low-noise operations. CapsuleBot combines the maneuverability of a bi-copter in the air with the low power consumption and low noise of a two-wheel self-balancing robot on the ground. To achieve this, we design an innovative mechanical structure named the actuated-wheel-rotor, which uses a servo motor and a brushless motor to function as both a tilting rotor in the air and an actuated wheel on the ground. CapsuleBot is equipped with two actuated-wheel-rotors, enabling it to achieve hybrid aerial-ground propulsion using only four motors, with no additional motors required compared to a bi-copter. Additionally, we develop comprehensive dynamics and control systems for both air and wheel mode, based on the bi-copter model and the two-wheel self-balancing robot model. A prototype of CapsuleBot is constructed, and its performance in terms of low power consumption and low noise is validated through experiments. Challenging tasks demonstrate CapsuleBot's capability to climb steep, fly over cliffs, and traverse rough terrains.

cs.RO↗

arXiv2024

Visual-Linguistic Agent: Towards Collaborative Contextual Object Reasoning

Multimodal Large Language Models (MLLMs) excel at descriptive tasks within images but often struggle with precise object localization, a critical element for reliable visual interpretation. In contrast, traditional object detection models provide high localization accuracy but frequently generate detections lacking contextual coherence due to limited modeling of inter-object relationships. To address this fundamental limitation, we introduce the \textbf{Visual-Linguistic Agent (VLA), a collaborative framework that combines the relational reasoning strengths of MLLMs with the precise localization capabilities of traditional object detectors. In the VLA paradigm, the MLLM serves as a central Linguistic Agent, working collaboratively with specialized Vision Agents for object detection and classification. The Linguistic Agent evaluates and refines detections by reasoning over spatial and contextual relationships among objects, while the classification Vision Agent offers corrective feedback to improve classification accuracy. This collaborative approach enables VLA to significantly enhance both spatial reasoning and object localization, addressing key challenges in multimodal understanding. Extensive evaluations on the COCO dataset demonstrate substantial performance improvements across multiple detection models, highlighting VLA's potential to set a new benchmark in accurate and contextually coherent object detection.

cs.CV↗

arXiv2024

Mitigating Stop-and-Go Traffic Congestion with Operator Learning

This paper presents a novel neural operator learning framework for designing boundary control to mitigate stop-and-go congestion on freeways. The freeway traffic dynamics are described by second-order coupled hyperbolic partial differential equations (PDEs). The proposed framework learns feedback boundary control strategies from the closed-loop PDE solution using backstepping controllers, which are widely employed for boundary stabilization of PDE systems. The PDE backstepping control design is time-consuming and requires intensive depth of expertise, since it involves constructing and solving backstepping control kernels. To address these challenges, we present neural operator (NO) learning schemes for the ARZ traffic system that not only ensure closed-loop stability robust to parameter and initial condition variations but also accelerate boundary controller computation. The stability guarantee of the NO-approximated control laws is obtained using Lyapunov analysis. We further propose the physics-informed neural operator (PINO) to reduce the reliance on extensive training data. The performance of the NO schemes is evaluated by simulated and real traffic data, compared with the benchmark backstepping controller, a Proportional Integral (PI) controller, and a PINN-based controller. The NO-approximated methods achieve a computational speedup of approximately 300 times with only a 1% error trade-off compared to the backstepping controller, while outperforming the other two controllers in both accuracy and computational efficiency. The robustness of the NO schemes is validated using real traffic data, and tested across various initial traffic conditions and demand scenarios. The results show that neural operators can significantly expedite and simplify the process of obtaining controllers for traffic PDE systems with great potential application for traffic management.

eess.SY↗

arXiv2024

Large time behavior of solutions to the 2D damped wave-type magnetohydrodynamic equations

In this paper, we are concerned with the 2D damped wave-type magnetohydrodynamic system (abbreviated as MHD-wave system). The purpose of this paper is to study the large time behavior of solutions to the MHD-wave system, espesically to investigate the influence of the bad term $γ\partial_{tt}b$ on the large time behavior. Rates of decay are obtained for both the solutions and higher derivatives in different Sobolev spaces with explicit rates of $γ$, which shows that the decay rates closely align with that of the MHD system under the same norm, for any fixed $γ>0$. In this sense, these decay rates are optimal.

math.AP↗

arXiv2024

Event-triggered Boundary Control of Mixed-autonomy Traffic

Control problems of mixed-autonomy traffic systems that consist of both human-driven vehicles (HV) and autonomous vehicles (AV), have gained increasing attention. This paper focuses on suppressing traffic oscillations in the mixed-autonomy traffic system using boundary control design. The mixed traffic dynamics are described by 4 x 4 hyperbolic partial differential equations (PDEs), governing the propagation of four waves of traffic, including the density of HV, the density of AV, the friction between the two vehicle classes from driving interactions and the averaged velocity. We propose an event-triggered boundary control design since control signals of the traffic light on ramp or the varying speed limit cannot be continuously updated. We apply the event-triggered mechanism for a PDE backstepping controller and obtain a dynamic triggering condition. Lyapunov analysis is performed to prove the exponential stability of the closed-loop system with the event-triggered controller. Numerical simulation demonstrates the efficiency of the proposed event-trigger control design. We analyzed how the car-following spacing of AV affects the event-triggering mechanism of the control input in mixed-autonomy traffic.

eess.SY↗

arXiv2024

Intention-Aware Planner for Robust and Safe Aerial Tracking

Autonomous target tracking with quadrotors has wide applications in many scenarios, such as cinematographic follow-up shooting or suspect chasing. Target motion prediction is necessary when designing the tracking planner. However, the widely used constant velocity or constant rotation assumption can not fully capture the dynamics of the target. The tracker may fail when the target happens to move aggressively, such as sudden turn or deceleration. In this paper, we propose an intention-aware planner by additionally considering the intention of the target to enhance safety and robustness in aerial tracking applications. Firstly, a designated intention prediction method is proposed, which combines a user-defined potential assessment function and a state observation function. A reachable region is generated to specifically evaluate the turning intentions. Then we design an intention-driven hybrid A* method to predict the future possible positions for the target. Finally, an intention-aware optimization approach is designed to generate a spatial-temporal optimal trajectory, allowing the tracker to perceive unexpected situations from the target. Benchmark comparisons and real-world experiments are conducted to validate the performance of our method.

cs.RO↗

arXiv2024

Mean-Square Exponential Stabilization of Mixed-Autonomy Traffic PDE System

Control of mixed-autonomy traffic where Human-driven Vehicles (HVs) and Autonomous Vehicles (AVs) coexist on the road has gained increasing attention over the recent decades. This paper addresses the boundary stabilization problem for mixed traffic on freeways. The traffic dynamics are described by uncertain coupled hyperbolic partial differential equations (PDEs) with Markov jumping parameters, which aim to address the distinctive driving strategies between AVs and HVs. Considering that the spacing policies of AVs vary in mixed traffic, the stochastic impact area of AVs is governed by a continuous Markov chain. The interactions between HVs and AVs such as overtaking or lane changing are mainly induced by impact areas. Using backstepping design, we develop a full-state feedback boundary control law to stabilize the deterministic system (nominal system). Applying Lyapunov analysis, we demonstrate that the nominal backstepping control law is able to stabilize the traffic system with Markov jumping parameters, provided the nominal parameters are sufficiently close to the stochastic ones on average. The mean-square exponential stability conditions are derived, and the results are validated by numerical simulations.

math.AP↗