Papers

Two properties of optimization-based controllers such as model predictive control (MPC) limit their use in space flight. The online computing time varies and can exceed the sampling period, and the closed-loop behavior carries no formal guarantee. Explicit MPC, where a parametric solution of the optimization problem is computed, replaces the on-board optimization with a lookup table of piecewise-affine functions with a fixed execution time. However, it has been regarded as only applicable to small horizons, since the number of regions bounds the memory the table occupies and admits only an exponential bound in the horizon. This paper builds on recent developments in parametric solutions of quadratic programs and nonconvex reachability analysis to provide an entire pipeline for spacecraft rendezvous controllers under the Clohessy-Wiltshire dynamics. For this application, efficient data structures can be constructed to encode the control law, both from a computing time and a memory standpoint. In addition, the controller can be verified offline with a guaranteed closed-loop stability certificate by resorting to reachability analysis with hybrid zonotopes. At a 51-step horizon the rendezvous law occupies 10.5 MB and is evaluated in under 300 ns. The occupied memory would fit for instance the CubeSat flight computer ARM9-class AT91RM9200 meaning that the horizon limitation does not hold when the partition is built with the parametric solvers and data structures shown in this paper.

This work investigates the uncertainty-aware joint pricing and matching problem for dynamic high-capacity ride-sharing services, where passengers are assumed to be price-elastic and decide whether to accept a ride-sharing offer based on the upfront prices provided by the platform. We formulate the studied problem as a two-stage stochastic program, where the first stage optimizes upfront price decisions for passengers, and the second-stage recourse problem captures passenger-vehicle assignment based on passengers' uncertain choices. To enhance computational efficiency, we introduce a novel relaxation-based gradient descent-guided search algorithm that leverages the problem's structural properties. Initially, the algorithm generates a feasible solution for the first-stage problem via relaxation. It then iteratively improves the solution via a search process guided by the derived gradient information. In particular, scenario reduction is applied to eliminate unnecessary scenarios when calculating the gradient, thereby reducing the overall computational burden. Numerical experiments demonstrate that, compared to solving the stochastic program directly, the proposed algorithm can accelerate computation speed by thousands of times while achieving optimality gaps of no more than 1.1%. Finally, we validate the benefits of considering passengers' choice uncertainty through large-scale simulation using real-world datasets and road networks over two large cities. The results demonstrate that, on average, the proposed method can increase the revenue by 5.2% and the service rate by 8.2% compared to the baseline approaches. This study provides a valuable reference for transportation network companies to design pricing strategies for ride-sharing to enhance service efficiency and improve revenue.

Classical finite control set based model predictive control (FCS-MPC) reduces the optimal problems to an enumerated searching algorithm, which is very simple and effective to control power converters. However, it requires a large amount of enumeration operations, increasing its computational load and hardware costs. In this work, we propose a new and simple predictive control technique with extreme efficiency. The proposal directly selects the optimal vector via determined visual maps, abstained solely requiring a rearrangement of the cost function and a simple fitting law, without any enumeration. It has been validated under a lab-constructed power converter and a set of commercialized low-cost digital controllers. Experimental data confirm that the proposal achieves the same control performance as classical MPC, with significant computational burden reduction (up to 88% for one-step prediction of two-level converters)

The low-rank alternating direction implicit (ADI) method is an efficient solver for large-scale Stein equations with low-rank solutions. This paper shows that, as in the continuous-time Lyapunov equation case, the low-rank Cholesky factor ADI (LRCF-ADI) method for Stein equations implicitly performs $\mathcal{H}_2$-pseudo-optimal model order reduction for discrete-time systems. This observation leads to an automatic shift-generation strategy, allowing LRCF-ADI to select subsequent shifts without user intervention. The standard LRCF-ADI method requires shifts outside the unit circle. We generalize the method to allow shifts anywhere in the complex plane, including on the unit circle. This extension enables numerical integration for frequency-limited Stein equations by interpolating the integrand at points on the unit circle. It also enables non-intrusive, data-driven balanced truncation and frequency-limited balanced truncation using experimentally measurable transfer function samples on the unit circle, without requiring access to a state-space realization. Numerical results for large-scale models demonstrate the effectiveness of the proposed methods as low-rank Stein equation solvers and data-driven model order reduction methods.

Harmonic programmed pulse width modulation (HPPWM), offering flexible harmonic regulation, is a promising solution for high-power energy conversion systems. However, most existing methods solve HPPWM in a continuous space while ignoring the finite timer resolution of practical digital controllers. This leads to a potential optimality deviation during deployment. Motivated by this, this paper proposes a Grid-Constrained State-Adaptive Particle Swarm Optimization (GCSA-PSO) strategy. By matching the solution space with practical timer constraints, GCSA-PSO directly searches for implementable pulse sequences in the discrete solution space, thereby improving deployment consistency while reducing the search burden. Moreover, a state-adaptive evaluation strategy is developed to assign different cost evaluations according to particle states, avoiding unnecessary evaluations and improving computational efficiency. Experimental data confirm that, compared with the classical method, the proposed method reduces the computational time while achieving higher control accuracy under practical digital-controller deployment.

Power-electronics systems should be treated according to their natural mathematical structure—inherent switching and discontinuities with piecewise continuous states. Therefore, the simulator should be organized around events, switching topologies, and topology intervals, rather than only around a continuous-time solver that later corrects or smooths discontinuities. This paper presents a simple, event-driven EMT architecture using native C kernels with Python orchestration. With this method, we distill the essential elements of discrete-event simulation applied to power electronics problems and thereby point toward a broad research thrust wherein mature ideas from discrete-event simulation are adapted for use in simulating power electronics circuits.

This paper studies the worst-case impact of constrained control inputs: an input seeks to maximize the average cost of some outputs, measured in the $L_2$ or $L_1$ norm, while remaining bounded in terms of other outputs. This problem template subsumes classical metrics such as the $H_\infty$ norm and the output-to-output gain, and arises in adversarial control, security assessment, and robust control. For linear time-invariant systems, we provide an exact semi-definite program (SDP) when there is a single constraint, and SDPs computing upper bounds when there are multiple constraints. We derive sufficient conditions, in terms of system zeros and relative degrees, under which the worst-case cost is unbounded, together with a constructive closed-loop modification that removes the unboundedness. From a security standpoint, unbounded values reveal structural limitations in detecting certain attack inputs. For positive systems, we provide scalable formulations whose complexity grows linearly in the state dimension: a scalable SDP for quadratic costs, and an exact linear program for linear costs. The results extend to nonlinear polynomial systems via a sum-of-squares program. We illustrate the results with numerical examples.

This study introduces a coupled multi-domain framework to simulate direct current (DC) arcs, integrating thermal, fluid dynamic, and electromagnetic phenomena. We derive a two-dimensional (2D) axisymmetric model by simplifying the magneto-hydrodynamics (MHD) equations and implement it in MATLAB/Simulink. The model captures the spatiotemporal evolution of temperature, fluid velocity, and magnetic fields within a wall-stabilized arc column. Simulations across a 10 A to 1500 A current range show a pronounced radial temperature gradient, with central axis peaks and rapid peripheral decay. The approach's accuracy is validated by aligning the simulated velocity and magnetic fields with established physical paradigms. Additionally, the model accurately reproduces the inverse current-voltage characteristics observed in classical models and empirical studies, while transient analyses confirm rapid millisecond-scale voltage stabilization. This accessible computational model provides a robust foundation for investigating DC arc dynamics and facilitates system-level simulations for advanced circuit breakers, welding technologies, and plasma applications.

Securing IEC 61869-9 Sampled Values (SV) is challenging because process-bus communication must satisfy stringent real-time constraints while supporting standardized high-rate publication profiles. This paper presents an experimentally validated security framework that combines lightweight per-frame authentication for operational SV traffic with post-quantum-capable key establishment protocol. For message integrity, the proposed method applies field-selective authentication employing optimized Chaskey-12 to reduce per-packet computational overhead. For trust establishment, the paper introduces an ML-KEM-based pairwise authentication and key-establishment procedure. The pairwise protocol is analyzed in the Quantum Random Oracle Model and is also verified with AVISPA tool under the Dolev-Yao adversarial model. A C-based publisher/subscriber prototype is implemented on a two-node process-bus testbed. Performance is evaluated across the eight IEC 61869-9 SV packet profiles using HMAC-256, AES-GMAC-128, Blake-2s, Chaskey-12, and a compiler-optimized Chaskey-12 implementations. These results indicate that optimized Chaskey-12 achieves ~90% lower latency than HMAC on SV packets. The proposed security framework is a practical and scalable candidate for protecting IEC 61869-9 SV traffic on resource-constrained digital-substation devices.

We present a machine learning framework for identifying sparse, interpretable models of dynamical systems directly from time-series data. Our approach parameterizes the underlying vector field using a neural architecture and trains it by minimizing a multi-step prediction loss over a finite horizon. To ensure numerical tractability, we optimize a mean absolute error objective averaged across prediction steps, and progressively increase the horizon during training. A key feature of this formulation is that it enforces consistency under repeated composition of the learned dynamics. As a result, the identified models exhibit significantly improved stability compared with approaches based on one-step regression of the vector field. When combined with sparsity-promoting regularization, this leads to parsimonious models that generalize beyond the training data. We demonstrate accurate recovery of systems exhibiting a wide range of behaviors, including stable and unstable fixed points, periodic orbits, and chaotic attractors. For chaotic systems, while long-term trajectory prediction is inherently limited by sensitivity to initial conditions, we show that multi-step training yields models with accurate short-term dynamics and strong agreement in long-time statistical properties, including mean, variance, and Lyapunov exponents. Moreover, we establish theoretical bounds linking trajectory error to statistical accuracy, providing a step toward a principled explanation for this behavior.

Autonomous agents increasingly interact with other independent agents, and such interactions typically admit multiple joint behaviors. When two agents prefer different ones, their independent strategies may be mutually incompatible and fail to reach a coordinated outcome; when they are identical, neither can differentiate its role when needed. Ideally, an agent should coordinate with any agent it encounters, regardless of which admissible joint behavior that agent aims to realize. We therefore propose a new form of strategy, the opinion-guided strategy, which keeps all the admissible joint behaviors available and postpones the selection to execution time, when the other agent's behavior reveals which one to realize. To realize this, nonlinear opinion dynamics are leveraged in a layered realization to guide the agent to a common admissible joint behavior in response to the other agent's evolving behavior, even without communication. We formally establish the conditions under which the strategy remains robust to every preference the other agent may hold. This robustness has an important implication: two agents running identical strategies can break symmetry when needed, a capability that conventional strategies lack. Three case studies across different applications show that the opinion-guided strategy coordinates with every randomly encountered agent, as long as it is willing to realize one of the admissible joint behaviors. One of them corresponds to a general-sum game: unlike conventional approaches devoted to finding a unique Nash equilibrium in advance, the opinion-guided strategy keeps every equilibrium open and guarantees the agents reach one, decided by their runtime interaction.

The concept of grid-forming (GFM) converters has gained great attention in the past years. However, it remains challenging to analyze and quantify the impacts of heterogeneous GFM devices (e.g., GFM energy storage systems, GFM wind turbines, GFM HVDC stations) on power system dynamics, especially when taking into account the complex interaction between GFM converters and grid-following (GFL) converters. To this end, this paper focuses on the decentralized and scalable stability analysis of power systems containing both GFM and GFL converters, where we use Davis-Wielandt (DW) shells to characterize the dynamics of the converters and the power grid. In particular, we analytically derive how integrating heterogeneous GFM converters affects the DW shell of the power grid and therefore the system stability. Our approach does not require the detailed parameters or control schemes of the GFM converters; instead, we define the local passivity and imaginary-axis indices of GFM converters to compactly describe their characteristics. These two indices can be conveniently obtained by testing a GFM converter and greatly simplify the stability analysis and computation when handling large-scale power systems.

Use of artificial intelligence in motor control applications that can be deployed within the microcontrollers need to comply with real-time demands of motor control systems. A supplementary controller approach that can be integrated within existing microcontrollers is presented. The proposed approach is implemented on a weeteq motor control integrated circuit and tested in the lab. A complete unsupervised motor control deployment solution was developed, and the real-time system response correction demonstrated under dynamic loads measured with and without the supplementary controller. The solution provides 100% coverage of real-time data at control loop sample rate and model inference period between 100usec and 1msec. The importance of latency for reduction of dynamic regulation margin during transient response is demonstrated with up to 68% reduction of the system response error. A novel key performance indicator based on principal components transform is introduced that provides a quantitative figure of merit for improvement of the transient response, in terms of the dynamic regulation margin, stability considerations and iterative improvements of consecutive regression model outputs. The significant events related to dynamic changes in the equipment and external operating conditions are detected at milliseconds resolution and recorded as highly compressed vectors representing deviation of the system response from linear steady state conditions. The resolution in time and accuracy of this vector data will enable a new level of system level optimisation that has not been possible using the time series data from IoT sensors in current equipment health monitoring solutions.

The rapid evolution of energy structures has positioned microgrids as pivotal components of next-generation power systems, offering enhanced resilience and renewable energy integration. However, the inherent low inertia, complex dynamics, and poor model conditions of microgrids necessitate advanced data-driven frequency control strategies. Although reinforcement learning (RL) has demonstrated certain potential and advantages, existing RL methods often struggle with generalization across diverse microgrid configurations and lack adaptability to unseen environments, particularly when explicit system parameters are unavailable. To address these challenges, in this paper, we introduce a novel prompt decision transformer (Prompt-DT) architecture for microgrid frequency control. Unlike traditional approaches that rely on hard-to-obtain environmental characteristic parameters, the proposed method leverages few-shot expert historical trajectories as prompts to guide autonomous perception and adaptive decision-making. In addition, we propose a context-aware training and execution mechanism utilizing self-supervised contrastive learning to enhance environment recognition and prompt utilization efficiency. In addition, a physics-informed prompt design technique that filters prompts based on cumulative reward and frequency volatility is proposed, ensuring high-quality physical guidance during online execution. Finally, to ensure generalization in unseen environments with limited data, we develop a lightweight finetuning approach that achieves performance comparable to full-parameter finetuning with minimal adjustments.

Missing lane-count observations can distort pressure-based signal decisions even when neighboring detectors remain operational. We propose a lane-movement probabilistic graph neural network (LMP-GNN) that uses the movement relations involved in pressure computation to predict a mean and standard deviation for each lane. Three rules convert these outputs into replacement counts using the mean alone, a fixed uncertainty discount, or a staleness-dependent discount. Observed counts remain unchanged, and the completed state is supplied to an unchanged Signed Max-Pressure controller. Evaluation covers reconstruction and uncertainty calibration, decision-time diagnostics, and closed-loop traffic performance. Across 4,333,392 masked lane events, reconstruction achieved a mean absolute error of 0.7873 vehicles per lane. In a separate stored-trace audit of 372 network-outage-seed cells, pressure-score error was strongly associated with phase disagreement, with a Spearman correlation of 0.929, identifying pressure fidelity as a key decision-level diagnostic. Across five fixed-demand CityFlow networks, the fixed-discount rule reduced accrued average travel time by up to 13.74% relative to road-level mean imputation under correlated missingness. It also reduced travel time at 60% random missingness, whereas mean imputation performed better at 80% and 90%. The selected model has 63,362 parameters and a median single-thread inference time of 0.983 ms on a central processing unit. These results support lightweight probabilistic lane reconstruction as a practical input to pressure-based control, with traffic benefits that depend on the missingness regime.

Robotic Fulfillment Centers (FCs) store inventory on shelves (pods) arranged in dense blocks. Retrieving a target pod that is buried deep in a block requires moving obstructing pods out of the way (i.e., digout). Multi-robot planners use parameterized cost functions to control digout behavior, producing a spectrum of strategies: at one extreme, obstructing pods are sent to other blocks (using more robots in travel lanes); at the other, pods are shuffled within the block (avoiding lane congestion but increasing extraction time). Each point on this spectrum has different downstream consequences for floor congestion and throughput. The optimal operating point depends on the specific facility configuration and shifts with operational conditions such as varying station demand and congestion patterns, making offline tuning impractical. We present an adaptive parameter tuning framework based on Extremum Seeking Control (ESC) that continuously adjusts planner parameters in response to measured throughput. ESC performs model-free optimization by perturbing parameters with sinusoidal dither signals and correlating perturbations with performance changes to estimate gradients, making it robust to the multi-minute delayed effects and credit assignment challenges inherent in large FC operations. Simulation studies demonstrate that the adaptive policy improves upon fixed policies across several conditions. We observe an improvement in throughput by an average of 5.0% across map and robot fleet size variations, and by 8.4% under dynamic operating conditions. This work eliminates manual parameter provisioning and enables real-time adaptation, providing a self-tuning paradigm for FC storage operations.

Fault diagnosis under unknown operating conditions remains challenging for dynamic industrial systems, as the distribution shift caused by changing operating conditions can significantly degrade the performance of diagnostic models in real-world applications. To address the problem, a condition-guided selective adaptation approach is proposed. Specifically, a novel continuous operating-condition adversarial learning strategy with progressive training is developed in the offline stage to enhance the generalization ability of the diagnostic model. During online deployment, residual operating-condition responses are exploited to identify reliable unlabeled samples from streaming data, which are then used to update the diagnostic model. Extensive experiments on real-world gearbox and motor datasets have demonstrated that the proposed framework outperforms state-of-the-art methods in diagnostic accuracy while maintaining relatively low test-time, showing its potential for practical industrial applications.

The static loadability limit, defined as the demand at which the equilibrium equations lose their solution, is the standard basis for interconnection screening of large new loads. This letter shows that when part of the demand varies periodically, as for data-center loads, the steady state is a forced periodic orbit whose loadability limit differs from the static one. The classical optimization argument is extended directly from equilibria to fixed points of the period map. At the limit, the monodromy matrix acquires a Floquet multiplier at +1, so the generic instability is a cyclic fold of the orbit rather than a saddle-node of equilibria. The limit is therefore a function of the forcing frequency, which no static computation can capture. Additionally, with the network Jacobian turning singular along the cycle, singularity-induced instability is extended to orbits. On a four-bus test system, a static margin of 2.5 p.u. shrinks to 0.53 p.u. near the swing frequency, the instability mechanism switches from voltage collapse to a rotor-angle fold, and cold starts fail at amplitudes where the orbit still exists. A static analysis reproduces none of these effects.

In this paper we discuss and analyze a method for lane-change intention anticipation of drivers on highways based on kinematic features and surrounding observations. The approach makes use of a recursive Bayesian filtering strategy, which can be interpreted as a hidden Markov model. We present two models for the likelihood term and show how they can handle missing observations. An additional focus of the work lies on calibrating the obtained probabilities in order to obtain reliable predictions. The introduced approach is evaluated in practice on the highD dataset and compared to several other baseline methods. Particular emphasize is put on evaluating the robustness and temporal consistency as well as calibration of the developed algorithm. Extensive numerical experiments allow for a careful performance assessment and detailed discussion of advantages and limitations of the different methodologies.

The ongoing energy transition challenges the stable operation of power systems and increases the need for rapid decision-making under uncertainty. While reinforcement learning has emerged as a promising framework for power system control and operation, existing applications typically focus on a single operational criterion, such as thermal security or small-signal stability. However, power system operation is inherently multi-objective and may involve trade-offs between objectives. This paper develops a unified-control deep reinforcement learning agent that maintains thermal security under stochastic load variations while steering the system toward operating points with improved damping of the most critical mode. Compared to a thermal-security-only agent and a business-as-usual policy, the proposed agent achieves a better balance among the operational objectives considered, with notably improved damping and negligible thermal-security violations. Finally, the operational value of increased critical damping is demonstrated under small- and large-signal disturbances, where operating points with higher damping lead to faster oscillation decay and improved critical clearing times.

The certification of Unmanned Aerial Vehicle (UAV) swarms for safety-critical operations requires verifiable design assurance. Airworthiness standards demand deterministic reliability, whereas multi-agent coordination algorithms execute non-deterministic models. This paper proposes a mixed-criticality architectural framework that applies SAE ARP4754B methods to swarm reconfiguration. First, a hardware-isolated Safety Monitor functions as a Run-Time Assurance (RTA) gateway, decoupling the flight-critical core from the non-deterministic Swarm Manager. Second, the monitor enforces formal safety contracts based on agent Health Vectors derived systematically from a Functional Hazard Assessment (FHA). Third, the framework propagates these Health Vectors to the collective planner to trigger fail-operational task reallocation, enabling intelligent swarm behaviors without compromising flight-critical isolation. Markov reliability modeling demonstrates that the $10^{-7}$ failures per flight hour Hazardous target is theoretically achievable for our SAIL IV scenario, provided the Safety Monitor meets $C_{monitor}>0.9991$, consistent with DAL B CMD/MON implementations.

Accurate electricity price forecasting is critical for smart grid stability, yet the heavy reliance on historical price lags in modern predictive models often obscures the fundamental physical drivers of market volatility. This paper proposes a regime-sensitive, explainable artificial intelligence (XAI) framework to unmask the hidden roles of load, climate, and calendar variables in the ERCOT Day-Ahead Market (2014-2024). Utilizing a Histogram-based Gradient Boosting Regressor (HGBR), we introduce a Suppression Ratio to quantify how price lags overshadow physical parameters. Our analysis reveals that while lags provide short-term memory during normal conditions, they fail to capture extreme dynamics; notably, their inclusion increased forecasting error (MAE/RMSE) during price spikes. By isolating these effects, the study revealed that price spikes are not merely the result of continuously rising temperatures, but rather emerge from the system reacting sharply within a specific temperature range. These findings underscore the importance of moving beyond lag-based models to uncover the true drivers of extreme price events.

Transportation networks, in particular multi-class transportation networks (i.e., networks with mixed vehicle types), are complex systems that are challenging to control. Recently, Deep Reinforcement Learning (DRL), which learns control policies from interactions with the environment, and Model Predictive Control (MPC), which uses a system model to optimize control inputs, have been increasingly utilized for transportation network control. However, nonlinear system dynamics and high-dimensional state spaces in large-scale networks limit DRL's learning capacity under time-constrained training and increase MPC's computation time, hindering real-time implementation with limited computational resources. Moreover, MPC depends on an accurate network model, which is often unavailable for complex systems such as multi-class transportation networks. This paper proposes a novel DRL-MPC framework for multi-class transportation networks that divides control authority between DRL and MPC, combining DRL's fast online computation and model independence with MPC's built-in optimization and constraint-handling capabilities. In the hierarchical framework, MPC operates at the higher level and determines low-frequency control inputs whose slower update rate accommodates its high computation time, while DRL operates at the lower level and determines high-frequency control inputs using its fast online deployment. The framework is evaluated on a multi-class freeway network against a hierarchical MPC controller and a hybrid state-feedback-MPC controller, including scenarios with model mismatch and noisy traffic demands. Results show that the proposed framework outperforms the hybrid state-feedback-MPC controller, substantially reduces online computation time compared with the hierarchical MPC controller, and provides more effective constraint enforcement under model mismatch.

This work addresses the limitations of CMOS at terahertz (THz) frequencies, where charge transit time and parasitic capacitances restrict the maximum operating frequency, fmax. As transistor dimensions shrink, reduced current handling capabilities further challenge CMOS, necessitating novel circuit design approaches for the THz domain. By leveraging the plasma characteristics of electron channels in CMOS transistors, this study explores a potential solution for THz signal amplification. Key mechanisms in plasma wave amplification within a continuum transistor array (CTA) formed by 28 nm fully depleted silicon-on-insulator (FD-SOI) CMOS transistors are investigated. A hydrodynamic transport model combined with Pierce's theory is presented to describe plasma wave propagation along the CTA. Simulations of gated amplifiers demonstrate the potential for THz signal amplification in advanced fabrication nodes. Finally, a proof-of-concept plasma wave amplifier operating at 700 GHz has been designed and fabricated, exhibiting amplification along the plasma wave propagation path.

Upgrading Single-Wire Earth Return (SWER) networks for smart grid capabilities requires a reliable communications technology. Narrowband Power Line Communication (NB-PLC) is a potential low cost solution. Real-world deployment is challenging due to the severe, frequency-dependent attenuation caused by complex earth-return paths and heterogeneous network infrastructure. To accurately characterize the communication channel, this paper develops a high-frequency (up to 300 kHz) digital twin of an operational SWER network. The digital twin integrates a segment-by-segment transmission line model with Vector Network Analyzer (VNA) measurements of physical grid hardware, replacing standard uniform assumptions with empirical component responses. Parametric sensitivity analysis demonstrates that distributed environmental factors, such as soil moisture and line sag, act as uniform magnitude offsets. Conversely, the conductor's magnetic permeability and local injection-transformer impedances dictate the channel's resonant spectral shape. Furthermore, cross-brand analysis proves that utilizing generic transformer models introduces significant prediction errors, confirming that accurate simulation requires manufacturer- and tap-specific data. Validated against in-situ field measurements from three transmitters, this digital twin replicates the path loss and dominant frequency-selective fading of the physical grid. Yielding a Root Mean Square Error (RMSE) between 4.65 dB and 9.73 dB across the three transmit paths, the model provides a practically reliable framework for deploying NB-PLC across rural SWER infrastructure.

Surface hopping is an attractive mobility mode for small-body exploration, but designing fuel-optimal hops on asteroid 433~Eros requires solving a nonconvex optimal control problem with an irregular polyhedral gravity field, thrust–mass coupling, and collision-avoidance constraints. We show that a physics-informed Bayesian Optimization (BO) warm-start—a Gaussian-process search over a single Bézier control point that requires no offline training—provides a more reliable initialization for Sequential Convex Programming (SCP) than the standard straight-line guess. The straight chord penetrates the asteroid on every inter-site transfer considered here and degrades convergence. The physics-informed BO reference feeds an SCP stage in which a log-mass change of variables convexifies the thrust–mass coupling and nearest-facet half-spaces enforce collision avoidance, and an automated Pareto time-of-flight sweep selects fuel-priority solutions without introducing bilinear terms. Applied to all 20 ordered transfers among five representative surface sites and validated under 10 random seeds, the framework tracks every trajectory to meter-level terminal accuracy in closed-loop Monte Carlo simulation, completes a five-site tour for one fifth of the propellant budget, and reduces mean $\Delta V$ by roughly one third relative to an idealized two-impulse ballistic trajectory estimate. A straight-line ablation credits the warm-start with cutting the mean SCP iteration count from 8.8 to 5.9 and removing the one convergence failure. A target-perturbation analysis further shows that the solutions vary smoothly with the landing target, with no jumps between local basins.

In the field of Human-Robot Interaction (HRI), achieving flexibility in human-accompanying within real-world environments holds great potential for various applications but also poses significant challenges. Traditional methods typically restrict robots to fixed positions relative to humans, such as tracking from behind, in front, or side-by-side, which limits robot adaptability in dynamic workspaces. This study introduces a novel human-companioning strategy that uses Reinforcement Learning (DRL) to enable mobile robots to dynamically adjust their tracking positions according to varying conditions. An interaction space is defined to capture the relationship between the human and the robot while considering the environment, which serves as the basis for state spaces in DRL to assist the robot in adapting to environmental changes. A human-robot companion controller is developed by integrating Model Predictive Path Integral (MPPI) control with Control Barrier Functions (CBF), ensuring that the robot accurately follows the target's movement in both position and orientation while avoiding obstacles and enhancing social acceptance and safety. The proposed approach is evaluated in real-world scenarios, both indoors and outdoors, and compared with other studies. The results show that the proposed method improves the success rate and tracking accuracy by at least 24% and 47%, respectively, while enhancing human comfort. Experiments demonstrate the robot's ability to flexibly accompany a person walking at speeds of up to 1.7 m/s, dynamically adjusting its strategy without being confined to a fixed position. Additionally, the robot respects the human's intimate space to ensure safety, comfort, and effective obstacle avoidance.

Vision-language-action (VLA) models can follow natural-language (NL) task instructions, but such instructions may not precisely specify safety-critical or spatiotemporal requirements on the resulting behavior. We introduce Logic-VLA, a formal-requirement-aware VLA that conditions on Signal Temporal Logic (STL) specifications supplied at inference time. Logic-VLA uses a syntax-graph-based STL encoder pre-trained to capture temporal logic semantics. Policy adaptation proceeds in two stages: STL-conditioned supervised fine-tuning on satisfying demonstrations is followed by trajectory-level preference optimization over matched satisfying-violating rollout pairs using a flow-matching surrogate for Identity Preference Optimization. This formulation improves formal requirement satisfaction while preserving the nominal NL task. We evaluate Logic-VLA in closed-loop quadcopter navigation simulation across randomized photorealistic environments and test generalization to STL formulas unseen during training. Across the evaluation benchmarks, Logic-VLA improves STL satisfaction rate over an STL-blind base policy by 24.8 to 40.7 percentage points (pp) while reducing nominal NL task success by at most 1.8 pp, showing that a single VLA can adapt its behavior to varying formal requirements without requiring a separate policy for each specification.

This work addresses the problem of applications that require wireless power transfer to devices embedded within conductive blocks. The emergence of eddy currents tends to generate a magnetic field opposing that of the transmitter, drastically reducing the coupling coefficient and, consequently, the efficiency of the process. A case study involving a titanium implant is presented and analyzed with a load designed to ensure a constant current of 1 mA. An experimental setup is assembled and evaluated to compare differences in the measured electrical parameters when the receiver is embedded in the implant, considering several solutions that mitigate or avoid the effects of eddy currents. The results show that such a system may be technically unfeasible if careful attention is not paid to the geometry of the cavity in which the receiver is positioned.

As a promising technique, fluid antennas enable adaptive radio environment management and interference mitigation through reconfigurable fluid port selections. In this work, to explore the benefits of fluid antennas for data freshness enhancement, we consider a fluid-antenna assisted status update system supported by multiple source nodes monitoring the same environmental status. We assume a subset of the source nodes are activated to report status updates with different periods. Each user is assigned to one source node and equipped with an fluid antenna to adaptively enhance the channel gain to assigned source node while mitigating interference from other activated source nodes. With maximal signal-to-interference-noise ratio (SINR)-based fluid port selection at all users, we formulate an optimization problem to minimize the maximum average age of information (AoI), where source node activation and assignment, i.e., source scheduling, is jointly optimized with the update periods of all activated source nodes. To optimally solve the resulting mixed-integer nonlinear problem, we first consider given source scheduling decision and apply performance achievability analysis. Aided by fixpoint theory, we propose an efficient bisection algorithm for optimal update scheduling. Based on these characterizations, we further propose a filtering algorithm which efficiently eliminates all non-optimal source scheduling decisions. The globally optimal joint solution is then obtained by combining the resulting optimal source scheduling with its corresponding optimal update scheduling. Numerical results validate the optimality of proposed solution and demonstrate the effectiveness of fluid antennas in enhancing data freshness.