Papers

We consider stochastic games with independent controlled chains and unknown transition kernels, where players observe only their local states and realized payoffs. We develop a fully online, decentralized, and uncoordinated mirror-descent algorithm that operates in the dual space of occupancy measures for approximating stationary Nash equilibrium (NE) policies. The algorithm uses a single transition/reward sample at every primitive time step, relies only on local information, and requires neither coverage of the joint state space nor synchronized episodes. Under uniform-ergodicity and finite-coverage assumptions, we show that, with high probability, the time-averaged fixed-comparator regret decays at the canonical $O(T^{-1/2})$ rate, up to logarithmic factors and polynomial dependence on the game parameters. In particular, the complexity depends on the cover times of the individual local state spaces rather than the product state space, avoiding exponential dependence on the number of players and the sizes of the joint state and action spaces. The resulting finite-time regret bound further yields an approximate coarse-correlated-equilibrium guarantee, which is natural for arbitrary reward functions since computing a stationary $\epsilon$-NE is PPAD-hard in this setting. Under an additional global variational-stability condition, we show that the same fully online algorithm converges asymptotically in the last iterate to a stationary $\epsilon$-NE. Our results provide a fully online and scalable learning framework for stochastic games with unknown independent chains. The algorithm can also be viewed as a primal-dual framework for Markov games that exploits the independence and local structure of the players' controlled transition chains.

Learning-based driving planners are usually trained and evaluated in open loop against logged trajectories. In closed loop, a trajectory with small displacement error can still stall the vehicle, steer it into a conflict with surrounding agents, or be executed with abrupt braking. We introduce Closed-Loop Refinement and Execution (CLRE), a hierarchical receding-horizon control framework designed to mitigate these failure modes while leaving the upstream planner frozen and adding no new learned model. The upper layer treats the nominal trajectory as a reference and solves a finite-horizon optimal control problem that trades route progress against interaction with predicted agents. Solving it from several initializations gives a candidate set, and a prediction-conditioned oriented-bounding-box (OBB) feasibility test retains only candidates whose minimum predicted OBB clearance over the horizon meets a threshold. The lower layer executes the lowest-cost survivor, or a route-centerline backup when none remains, through the tracking controller supplied with the planner, augmented by a range-based speed bound and a saturated proportional braking law. In closed-loop simulation on the 220-route Bench2Drive validation set with VAD as the upstream planner, CLRE raises the driving score from 42.26 to 55.89 and route completion from 55.69 to 71.51, and reduces collision events from 117 to 97.

This paper studies communication-aware safe control for discrete-time linear multi-agent systems under limited information exchange. The main challenge lies in the coupling between remote-state estimation and safe controller design, since estimation errors affect the state evolution through the controller gains, while the controller design must account for the resulting observer-induced state perturbations to guarantee safety. To address this challenge, a distributed k-hop observer is developed to reconstruct unavailable remote states, and a uniform observer-error bound is derived. The effect of the observer-induced state perturbation on closed-loop safety is accounted for in both the construction of local {\epsilon}i-robust safe invariant (RSI) sets and the enforcement of pairwise relative-state safety constraints. The resulting safety conditions ensure that all agents remain within their local RSI sets while all pairwise relative-state safety constraints are satisfied. A linear matrix inequality (LMI)-based optimization method is developed to jointly synthesize the distributed observers, local controllers, and RSI sets. A case study illustrates the effectiveness of the proposed method.

Machine learning models are increasingly used in feedback control loops for nuclear fusion, where inference speed and predictable timing are critical. We summarize lessons from models deployed for control on the DIII-D tokamak and develop a benchmark to compare inference backends across ten neural networks and model components from fusion control and diagnostic pipelines. For models greater than five million parameters, the CPU backends take tens to thousands of milliseconds, while GPU inference is substantially faster, suggesting an upper limit on CPU-oriented development for control. These results show why the deployment backend must be selected together with the model and its control-cycle budget.

This paper develops a distributed adaptive neural interval observer for unknown nonlinear systems with locally incomplete measurements. Each sensor node constructs lower and upper state estimates using its local output and neighboring observer information, while unknown nonlinear dynamics are approximated by adaptive neural models. A distributed adaptation mechanism guarantees bounded estimation and weight errors, whereas a cooperative realization preserves the componentwise interval property. To enhance neural-weight convergence without requiring persistent excitation, a finite experience-replay integral concurrent-learning mechanism is incorporated into the adaptation law. For non-Metzler error dynamics, a Sylvester-based coordinate transformation is introduced to recover a Hurwitz–Metzler distributed realization. The theoretical developments are validated through a nonlinear distributed estimation example.

This paper develops an event-triggered fixed-time integral reinforcement learning framework for optimal control of unknown nonlinear systems. An integral data-driven identifier is first used to reconstruct the unknown dynamics, after which an inverse-optimal formulation is employed to construct a fixed-time running cost. A learning law satisfying the practical fixed-time property is then derived. Previously collected data, or data obtained during a finite excitation interval, are stored in an experience-replay buffer and incorporated into the weight-update law. This avoids the persistent-excitation condition, which is often difficult to satisfy in practical operation. To reduce communication and control updates, an event-triggered mechanism is introduced. The paper shows that, under the event-triggered implementation, the closed-loop system still achieves practical fixed-time stability, while the proposed triggering rule guarantees the exclusion of Zeno behavior. Finally, a nonlinear example is presented to verify the theoretical results developed in the paper.

This paper develops a meta-multi-agent reinforcement learning (meta-MARL) framework to enable fast adaptation of interactive policies in a multi-agent system (MAS). Meta-reinforcement learning (meta-RL) enables agents to rapidly adapt to new tasks/environments using a bi-level optimization mechanism. However, existing meta-RL generally focuses on single-agent systems. Extending these frameworks and algorithms to multi-agent systems poses additional challenges, as tasks are characterized by not only the environment but also agents' strategic interactions. To address these challenges, we model multi-agent reinforcement learning (MARL) problems as Markov games (MGs) and develop a meta-MARL framework for rapid interactive policy adaptation across a distribution of MGs. A new concept, called meta-NE, is defined to describe the desired solution concept in a meta-MARL problem. Sufficient conditions for the equivalence between a meta-NE and a stationary point of the gradient-play-based meta-MARL algorithm are established. Our evaluation on autonomous-driving tasks demonstrates that the proposed meta-MARL method achieves faster adaptation than pretrained MARL baselines, validating the effectiveness of our framework.

We establish non-asymptotic sample complexity bounds for the least-squares estimation of vector autoregressive models for exponentially stable systems with heavy-tailed noise based on a single observed trajectory. By assuming i.i.d. noise, bounded noise covariance, and persistent excitation, we show that the estimation error is $\widetilde{\mathcal{O}}(r^{1/2}T^{-1/2+1/p})$ under bounded $p$th moment for $p > 2$, where $T$ is the number of samples, $r$ is the noise dimension, and $\widetilde{\mathcal{O}}(\cdot)$ hides logarithmic terms. We also introduce a unifying approach to sample complexity analysis applicable to broad classes of noise distributions and showcase this by deriving error bounds for sub-exponential and sub-Gaussian noise distributions. Finally, we specialize our analysis to autoregressive models with exogenous inputs and show that the dimension factor of the error bound is independent of the model order.

Large language model (LLM) based agents are often criticized for lacking spatial understanding and mainly exploiting statistical text patterns. We investigate their spatial comprehension through an architecture combining geometrical tools with a LLM serving as a high-level orchestrator in grid-world environments. The agent first collects geodesic trajectories, which are then vector-quantized to extract a representative subset. Offline, the LLM associates a natural language description of the underlying behavioral patterns to each selected trajectory, making it a tool. Online, the LLM chooses the appropriate tool conditioned on the current state and goal. Low-level control is handled by primitive actions that execute the trajectory associated with the tool. From an agentic AI perspective, this approach separates learning into two levels: tool discovery is handled through unsupervised quantization of trajectories, while reasoning and decision-making are handled by the LLM. We test the approach in a partially observable dynamic 2D grid environment with an open vision-language model (Qwen3.6-35B-A3B). Pairing the geometry-derived tool library with an agent-centered zoom tool and a collision detection tool lets a fast, non-reasoning configuration match the goal-reaching rate of a much more costly chain-of-thought version, while cutting the cost of a decision from minutes to seconds.

The rapid proliferation of data centers (DCs), driven by cloud computing and artificial intelligence (AI), has led to massive energy demand and carbon emissions, posing significant sustainability challenges. Carbon-aware optimization in geographically distributed data centers has been widely studied. Most existing approaches mainly focus on operational carbon emissions from server usage. However, existing literature often ignores workload-induced thermal stress, which accelerates nonlinear hardware degradation. This leads to more frequent server replacements and ultimately increases embodied carbon emissions. To address these limitations, we propose a comprehensive carbon life-cycle modeling framework for distributed data centers. Apart from operational carbon emissions, this work combines workload scheduling with a utilization-dependent exponential aging model to evaluate long-term carbon costs from server degradation. In order to solve the proposed optimization model in an online and privacy-preserving manner, an enhanced Lyapunov framework with time-varying queue shifting (TVQS) is first introduced to handle system uncertainties. Then, a zero-sum perturbation-based alternating direction method of multipliers (ZSP-ADMM) framework is developed to enable distributed coordination across geographically separated data centers while protecting locally exchanged workload information. Simulation results demonstrate that the proposed approach achieves up to 13.0% lower carbon emissions and 12.6% lower operational costs compared with benchmarks.

This paper addresses the control of multi-input strict-feedback nonlinear systems subject to a joint capacity constraint, in which the admissible input set is a coupled subset of the individual actuator limits. Unlike existing constraint-handling methods that enforce actuator bounds channel by channel and may unnecessarily suppress admissible control directions, we develop an Anisotropic Joint-Admissibility-Preserving Input Realization (AJ-APIR) framework that explicitly exploits the geometry of the joint constraint. The proposed realization constructs a state-dependent gain matrix whose spectral decomposition separates the commanded input into normal and tangential directions relative to the constraint boundary. The normal component is attenuated as the boundary is approached, while the tangential component is preserved, which allows the admissible control effort to be redistributed without loss of tracking authority. Integrated with a backstepping controller, the AJ-APIR framework guarantees forward invariance of the joint admissible set for all time. We establish exponential convergence of the tracking error to zero together with uniform boundedness of all closed-loop signals, and characterize the resulting command-demand behavior under the joint constraint. Simulation results for a representative second-order, two-input nonlinear system subject to a power-budget constraint demonstrate the efficacy of the proposed method to enforce the joint input constraint.

Whether a forced-oscillation source can be uniquely localized depends jointly on the available measurements and the candidate intervention dictionary. This paper considers a single unknown constant-amplitude sinusoid acting through one of physical intervention channels. Because its amplitude and phase are unknown, each candidate harmonic response is observable only up to a nonzero complex scalar and therefore defines a projective ray in measurement space. A necessary-and-sufficient condition is derived for exact localization, together with a weighted nearest-ray locator and a deterministic recovery bound based on projective separation. The paper proposes a framework distinguishing four mechanisms of source localization failure: mechanism mismatch, feature-projection loss, structural nonidentifiability, and poor conditioning or model error. Numerical studies on the Kundur two-area system illustrate well-separated recovery, mechanism-dependent terminal decisions, measurement-induced ambiguity, and the sensitivity of nearly collinear signatures to model perturbations. Then, IEEE–NASPI Contest models are used to further examine broad candidate dictionaries, terminal-feature source coverage, and ambiguity removal through measurement augmentation. The results show that correctly locating the source bus does not by itself establish unique identification of the internal forcing channel or robustness to measurement and model uncertainty.

This paper describes a skill-based agentic framework for power-system studies using Model Context Protocol (MCP)-connected engineering tools. A custom MCP server was developed to expose Siemens PTI PSSE functions for power-flow analysis, dynamic simulation, result extraction, and model-validation workflows. Two implementation pathways built on a programmable OpenAI Agents software development kit (SDK) and a Claude Code command-line interface (CLI) were evaluated, both using reusable skills, subagents, MCP tools, data-repository connections, and local shell/Python execution. Both frontier-model-based implementations successfully executed representative study tasks. Success was evaluated based on task completion, output accuracy, and the need for human expert interventions. Results based on public datasets show that agentic systems can greatly accelerate power system dynamic simulation process for transmission planning studies leveraging industry-grade simulation platforms. This points toward a shift in transmission planning practice, where agentic systems could handle routine simulation setup and result extraction, allowing engineers to focus expert judgment on scenario design and interpretation rather than tool operation.

In this paper, we present a modular proportional-integral-derivative (PID) controller whose computation and mode logic are executed by a higher-level state machine. Inspired by real-time safety-critical applications where computational load, actuator chattering, sensing error and noise are design challenges, the proposed algorithm wraps a standard PID inside a finite state machine with three states (pidInit, ErrorOutRange, ErrorInRange). The goal is to regulate a desired reference within a safe region of operation while reducing actuator chattering and creating a sizable hold band over the range of operation. This design is modular and can be easily integrated into a higher-level state machine with multiple low-level loops and states. The algorithm also has low computational complexity and is suitable for embedded hardware deployment. We demonstrate the effectiveness of the algorithm in simulation on the two test benches of a published event-based PID benchmark. The algorithm computes the control updates significantly less often than the time-triggered PID and less often than both dominant event-driven controllers, while keeping a fairly equivalent control performance profile. Furthermore, we also present some experimental results of the designed algorithm for a real-time flow control.

Cable-suspended aerial manipulation offers a lightweight architecture for cooperative transportation and physical interaction, yet the passive mechanical response perceived at the load remains insufficiently understood and systematically exploited. This work interprets aerial vehicles as movable compliant anchors and develops a gravity-aware quasi-static theory for predicting and shaping the passive Cartesian stiffness of a suspended load. The formulation applies to an arbitrary number of aerial vehicles connected to a point load by taut, straight, inextensible cables. At a selected gravity-loaded equilibrium, aerial-anchor compliance and transverse cable geometric compliance combine in series within each leg, while the leg stiffnesses act in parallel on the load. For isotropic aerial-anchor behavior, each leg is exactly equivalent to a virtual unilateral elastic cable, revealing an axial–transverse stiffness decomposition governed by the equilibrium tension. These results define a nonlinear map from commanded-anchor configuration to passive load stiffness, whose differential enables local constraint-preserving shaping through anchor repositioning. A dynamic rigid-body validation framework with nonlinear vehicle control, elastic-damped tendons, and environmental contact is defined to assess when and to what extent the derived stiffness remains predictive beyond the assumptions of the analytical model.

Long-term battery energy storage system (BESS) planning often relies on simplified degradation, operational, and temporal representations to maintain computational tractability, yet their effects on lifecycle conclusions are not well understood. This paper assesses the modeling fidelity needed for long-term lifecycle evaluation of BESS designs used in planning studies. A 20-year grid-connected microgrid is sized using a degradation-naive planning model, after which the installed portfolio is fixed and evaluated through sequential lifecycle validation. The reference representation combines nonlinear calendar and cycle aging, C-rate-dependent efficiencies, state-of-health-dependent performance, self-discharge, battery replacement, and full 8,760-h chronology. Battery-model hierarchies, targeted ablations, linear degradation surrogates, health-update intervals, temporal reductions, and combined simplifications are compared using lifecycle cost, replacement timing, state of health, and energy adequacy. The reference case produces replacements in years 9 and 18, a $111.25 million lifecycle net present cost, and 24.01 MWh of cumulative energy not served. Omitting calendar aging eliminates both replacements and understates lifecycle cost by 35.1%, whereas a separately calibrated linear surrogate model reproduces both replacement years and limits the cost deviation to 0.2%, although energy not served remains 31.8% below the reference. A peak-informed calibrated 12-day representation preserves replacement timing but reports zero energy not served, while replacing the preserved peak day with the maximum daily-energy-deficit day substantially overstates energy not served because representative-day closure alters the battery state surrounding the critical event. The results show that modeling fidelity is metric-dependent and should be selected according to the lifecycle outcome to be preserved.

Echolocating bats can navigate dark and cluttered spaces using echolocation. Over a decade ago, BatSLAM showed that a robot with a biomimetic binaural sonar can build a topological map of the environment, by recognizing places from the received acoustic signals. Sonar place recognition, however, is ambiguous by nature: corridors produce nearly identical echo trains, and wrong loop closure can collapse the topological map. In this paper, we introduce BatSLAM 2.0, a novel sonar-only SLAM system built from three elements: an updated acoustic front-end, a sequence verifier that tracks and verifies loop closure candidates and a pose graph implemented on a high performance factor graph framework. The system was thoroughly evaluated both in simulated as well as real world recordings. In both cases, the BatSLAM2.0 algorithm shows the capability of robust topological map creation, countering map collapse, and robust scaling of map size.

Control design for high-order converters with nonlinear dynamics is generally difficult. Nonlinear controls introduce formidable model complexity whereas linear controls suffer from poor disturbance rejection. Our objective in this paper is to develop a simple neural-network-based controller that learns the control law from supervised trajectories generated by a model-based bounded inversion framework. First, we establish strong invertibility for the converter models. Next, we prescribe the desired closed-loop behavior through a trajectory model that defines the target dynamics. We then invert the converter dynamics numerically to obtain the corresponding duty-cycle and state trajectories. We use these model-based trajectories as supervision and train a neural network to approximate the resulting state-to-duty control law. The results show that the learned controllers accurately recover the inversion-based control action and achieve effective regulation under load and input-voltage disturbances. We validate the method on fourth order Cuk, SEPIC, and zeta converters.

Ramp products are widely used in regional electricity markets to procure intertemporal flexibility in anticipation of net demand changes. However, the design of such ramp products often lacks a clear specification of ramping duration, potentially leading to infeasible dispatch solutions. This paper develops a duration-aware ramp adequacy screening method that evaluates whether the currently committed and dispatched fleet can meet the anticipated net-demand ramp requirement across different durations. A negative ramp adequacy margin identifies an insufficient-duration set in which the fleet lacks adequate ramp capability. Evaluating this margin across duration supports product-duration selection, while tracking it over time provides a metric for assessing ramp adequacy under different products and dispatch policies. We further formulate rolling-window ramp-reserve procurement with horizon-dependent forecast uncertainty and show that a product can affect ramp capability beyond its designated duration through changes in dispatch positioning. Building on this framework, we develop a forecast-free ramp-reserve scarcity dispatch policy that prioritizes resources according to their remaining ramp-up durations and, in the transmission-unconstrained setting, achieves the same minimum operational security loss as a perfect-foresight benchmark. Studies on a 10-generator system and a 2751-bus synthetic Texas grid demonstrate the value of the proposed framework for early detection of ramp scarcity, product design and evaluation, and screening-guided emergency dispatch.

Physical LiDAR attacks are often evaluated using fixed primitives and manually selected parameters, despite their strong dependence on surrounding traffic. We present TACTIC, a scene-aware framework that uses a multimodal large language model (MLLM) to coordinate state-adaptive roadside LiDAR attacks. Under a gray-box threat model, TACTIC relies only on an attacker-operated roadside perception stack, without accessing the victim LiDAR's native point clouds or internal processing. Local perception provides metric vehicle states, while the MLLM combines these measurements with roadside imagery to infer relational traffic context and construct a semantic scene graph. Based on this representation, TACTIC selects and configures two complementary primitives: push-away, which shifts the perceived range of a lead vehicle, and phantom-obstacle braking, which triggers emergency braking through obstacle injection. Measured traffic states and empirically calibrated constraints ground the generated tactics in physically feasible operating regions. To accommodate MLLM latency, TACTIC overlaps reasoning and execution asynchronously while high-rate local perception detects scene changes and triggers replanning. Across 280 randomized CARLA trials, the full policy achieves a 100% collision rate, compared with 35% for a fixed rule, 60% for random selection, and 75% for a restricted LLM using mode selection with default parameters. Joint physical-and-image input achieves 100% success, versus 65% with physical measurements alone and 75% with imagery alone, while asynchronous $\Delta$ refresh reduces scene-mutation response from 7.4 s to 2.0 s. These results show that scene-dependent tactical planning can expose context-sensitive LiDAR failure modes that fixed attack policies may miss.

While traffic modeling and control rely upon effective calibration of macroscopic traffic simulation models like METANET, recent empirical work shows these models can exhibit severe sensitivity to input noise. This paper explains this phenomenon through a string-stability analysis, demonstrating that a calibrated METANET model can amplify small additive perturbations to boundary conditions along the corridor, causing the simulated state to diverge from the nominal baseline. The input sensitivity in off-nominal cases may compromise the model's capabilities for counterfactual analysis, which is required for use cases of interest like the design of large-scale variable speed limits. To address these issues, this work demonstrates how a dynamic, time-varying approach to calibrate model parameters can achieve robustness and improved accuracy of the resulting macrosimulation. We show analytically that under stated regularity and perturbation assumptions, dynamic calibration achieves a tighter cost deviation bound than static calibration, and we validate this behavior in highway environments with synthetic and real-world data. Ultimately, this can enable more trustworthy traffic simulation and more effective evaluation of control strategies.

Continuum robots are well suited for gentle manipulation because of their inherent compliance and ability to adapt to complex environments. However, their continuously deformable structure makes accurate configuration estimation challenging, particularly when external vision systems are unavailable or obstructed. In this work, we present an embedded pose sensing framework that combines inertial measurement units (IMUs) and active magnetic fields to estimate the robot configuration without relying on external cameras. The angular measurements from the IMU and magnetic-field references are fused to improve local orientation estimation and reduce accumulated orientation error during operation. This pose sensing scheme achieves an update rate of 16.7~Hz, allowing real-time feedback. The proposed system is experimentally validated through closed-loop control, where the estimated robot configuration is used to maintain the end-effector at a desired position while interacting with an object. These results demonstrate the potential of distributed magnetic–inertial sensing for real-time pose estimation and closed-loop control of continuum robots.

This letter develops a data-based method for designing state feedback for a discrete-time linear system under bounded disturbances. For each admissible feedback gain and scalar design parameter, a Lyapunov equation determines an invariant ellipsoid: a region that the state cannot leave under the permitted disturbances. We optimise the gain and parameter to minimise a trace-based measure of the resulting output enclosure. At each parameter value, value iteration gives a lower cost bound, while separate controller evaluation gives an achievable upper bound. These bounds also let us assess whole parameter intervals without evaluating every point. We prove that the search stops after finitely many evaluations with a stabilising state-feedback gain whose objective is within any prescribed absolute tolerance of the infimum over the chosen ellipsoid family. Neither an initially stabilising gain nor attainment of the infimum is assumed. The guarantee assumes exact arithmetic and a sufficiently informative batch of exact measurements, including disturbances during data collection; the resulting feedback uses only the state. A position–velocity example illustrates the bounds, controller checks, and computational cost.

How much unknown process noise can a bounded experiment tolerate while still establishing that the plant can be stabilized? We answer this question for scalar discrete-time systems with bounded inputs, exact state measurements, and a disturbance-energy budget proportional to the experiment length. We distinguish individual stabilizability of every consistent model, stabilization by one common gain, and a common quadratic certificate. We determine their exact noise ceilings for every drift. The last two requirements coincide; individual stabilizability generally tolerates more noise. Bounded periodic inputs approach the ceilings, while adversarial disturbances prevent their attainment. We also determine the long-horizon limits. For unstable plants, a short experiment can establish individual stabilizability at noise levels where every sufficiently long experiment fails. The same obstruction yields upper bounds for multidimensional systems with real eigenvalues. Input design may use plant knowledge, but certification uses only the recorded data and noise bound. The results therefore provide fundamental robustness benchmarks for unknown-plant designs.

Neural trajectory predictors can reach low prediction error while violating dynamics, actuator limits, or state constraints, especially when controls are unobserved and dynamics are partially specified. We introduce the Markovian Dynamics Enforcer (MaDE), a time-invariant post-hoc operator mapping state-transition proposals onto a learned feasible dynamics manifold, trained on feasible states without ground-truth controls. For each transition it infers a control and recomputes the state through a completion model of known physics plus a learned residual. It then corrects that control by gradient-based inequality reduction, so inequality satisfaction is best-effort within an iteration budget. Since every correction iterate re-enters the completion model, the returned state is dynamically consistent by construction relative to that model and the supplied previous-state anchor. MaDE drives dynamics residuals to essentially zero on fully specified simulated systems, and on an underspecified system leaves a smaller true-dynamics residual than the baselines. Designed to attach to arbitrary predictors, the frozen operator is evaluated downstream of recurrent, structured state-space, and transformer predictors. On recorded vehicle trajectories the one-step residual against a kinematic bicycle model is 0.0071 to 0.0072 for MaDE and 0.1703 to 0.1714 for raw predictors. MaDE raises average displacement error by a factor of 1.57 to 1.83.

Laser-based deorbiting has long been proposed as a promising technique for mitigating the growing population of orbital debris, particularly decimetre-scale objects – large enough to cause catastrophic collisions yet too small to be economically captured by current active debris removal (ADR) missions. However, the practical implementation of laser-based approaches remains constrained by several factors. Ground-based systems suffer from atmospheric absorption, scattering, and turbulence, which degrade beam quality and limit effective energy delivery. Space-based platforms benefit from superior propagation efficiency, enabling more compact and lower-power systems, but require complex, high-cost orbital infrastructure with demanding deployment, maintenance, and scalability challenges. This paper proposes a stratospheric balloon-based concept for laser debris removal, exploring the intermediate regime between ground-based and space-based approaches. Operating above 99% of the atmospheric mass, such a platform may enable ultraviolet (3HG/4HG) laser operation – largely precluded from ground level by ozone absorption – with improved propagation and ablation coupling efficiency, while presenting a different set of engineering constraints than orbital systems. Rather than a fully developed system, this work focuses on the conceptual design and underlying rationale. We describe the main architectural elements, discuss the expected operational envelope, and position the concept against existing ground- and space-based solutions, identifying potential advantages as well as key challenges and limitations requiring further investigation. Developed within STRATOLASER, an EIC Pathfinder Challenge project targeting TRL 4 through stratospheric balloon experiments.

In this work, we present a simple method to engineer a small neural network to control power converters. Learning-based methods have emerged as a promising approach for power converter control over wide operating point variations. However, many of these approaches do not exploit the analytical structure of converter dynamics and, therefore, search over unnecessarily broad policy classes. We address this gap with a systematic framework that combines model-based trajectory generation with policy learning. First, we use differential flatness of averaged converter models to generate feasible state and constrained duty cycle trajectories where duty acts as a control effort signal. Then for policy learning, we use these model-generated trajectories to construct labeled state-to-duty data and train a multilayer perceptron to approximate the control law with a static map. We apply this framework across buck, boost, and buck-boost converters through topology-dependent flat output selection and a common learning pipeline. Simulations show close agreement between learned and model-generated duty trajectories and stable regulation under large synchronous input and load disturbances.

To address parameter sensitivity commonly in interior permanent magnet synchronous motor (IPMSM) sensorless control, this paper proposes a parameter-robust control framework by extending the adaptive flux observer from surface-mounted PMSM to salient-pole machines. First, parameter mismatches are interpreted as an equivalent flux vector with $d$-axis and $q$-axis components. Permanent magnet flux, $d$-axis inductance, and resistance mismatches are mapped to the $d$-axis flux component, whereas $q$-axis inductance mismatch is mapped to the $q$-axis component. Then, a scalar adaptive flux update is designed to compensate the $d$-axis flux variation by tracking the equivalent flux magnitude. Since the $q$-axis component rotates the observed flux direction and cannot be eliminated by scalar adaptation, a high-frequency $q$-axis voltage injection method is introduced to identify the $q$-axis inductance. Meanwhile, stability analysis proves bounded equivalent flux magnitude tracking and identifies the residual tangential flux responsible for position error. Finally, experimental results show that the proposed method does not require precise resistance, $d$-axis inductance, or flux, while the $q$-axis inductance is provided by online identification.

With the rapid development of the low-altitude economy, low-altitude operations are booming, where complex missions require collaborative efforts among multiple heterogeneous low-altitude aircrafts (LAAs). Specifically, different LAAs assume distinct roles: some for sensing, some for communication, some for computing, and others for mission execution, together forming a sensing-communication-computing-control (SC3) closed loop, akin to a reflex arc. To enable efficient coordination in such multi-LAA swarms, we introduce the concept of operational-capability entropy (OCE) to quantify the effective work capability of operational LAAs. Accordingly, by jointly considering heterogeneous OCE and channel conditions among LAAs, we formulate the power allocation problem with the goal of minimizing the linear quadratic regulator (LQR) cost, which serves as a metric for mission efficiency. The resulting complex optimization problem is decomposed into two convex subproblems that are solved iteratively, with closed-form solutions derived for each. Simulation results demonstrate that the proposed mission-oriented adaptive power allocation scheme significantly outperforms traditional ones.

Grid-forming (GFM) inverters are gaining attention as a promising alternative for conventional synchronous generators in the modern power systems. Unlike conventional synchronous generators, GFM inverters have limited overcurrent capability that makes them vulnerable during large disturbances. During disturbances i.e., voltage and frequency dips, GFM inverters are pushed into current-limited operation to protect the semiconductor switches. This causes the internal angle of the GFM inverters to accelerate and lose synchronism with the rest of the grid. To address this limitation, this article proposes a transient triggering grid-forming (TTGFM) synchronization control to enhance the synchronization stability performance under voltage and frequency dips. This method uses two feedback signals; terminal voltage and the difference between unsaturated and saturated power to adjust the internal angle which is generated by power synchronization loop (PSL) of the GFM inverters. These two signals manipulates the internal reference angle generation that act as a virtual braking mechanism. This mechanism limits the angle acceleration during voltage and frequency dips without requiring an extra supervisory signal or parameters to tune. The proposed method is benchmarked against two state-of-the-art synchronization enhancement schemes and validated through high-fidelity electromagnetic transient (EMT) simulations with a grid dynamic equivalent (GDE) model in MATLAB/Simulink\textsuperscript{\textregistered}. An analytical framework is developed to derive the synchronization instability mechanism and the critical limits of the stability margins. Generalization of the GDE model further shows that the TTGFM control is not restricted to a single configuration but is applicable to any standard benchmark system.