Papers

Open Radio Access Networks (O-RANs) bring intelligence to disaggregated RANs through virtualized platforms and open interfaces, but the stringent latency budgets of the RAN Intelligent Controller (RIC) and heterogeneous traffic conditions make real-time deployment costly. Centralized learning is ill-suited to the RIC due to its computational burden, timing constraints, and communication overhead, motivating Federated Learning (FL) as a distributed alternative. We formulate FL in O-RAN as a Mixed-Integer Nonlinear Program (MINLP) that minimizes communication cost and learning time under heterogeneity in User Equipment (UE) compute, traffic, and channel conditions, and decompose it into a UE Assignment (UA) subproblem and a Computation and Bandwidth Allocation (CBA) subproblem. We then propose FedRIC, a heterogeneity-aware FL framework combining compressed gradient descent, slice-aware resource allocation, and multilevel hierarchical aggregation. Experiments show that FedRIC outperforms FedCOM, FedAvg, and QFedAvg in convergence rate, communication cost, and latency.

Neural emulation need not require learning a substitute for known physics. We investigate PolyNets that couple a continuous-time neural representation of polynomial dynamics with a simple recurrent network for discrete-time integration. When the governing coefficients are known, the resulting tensor graph requires no training and preserves the prescribed numerical scheme while enabling efficient hardware execution and parallel simulation. Using the one-dimensional Korteweg-de Vries equation, we compare PolyNet with CPU and GPU finite-difference Runge-Kutta solvers and three trained neural models: a state-map emulator, a physics-informed predictor, and a learned-tendency integrator. Analytical soliton solutions provide a reference for amplitude and phase errors across grid resolutions, 10^6 dispersively scaled time steps, ensembles of up to 10^4 trajectories, and varying initial amplitudes. PolyNet achieves accuracy comparable to the numerical solvers, including at amplitudes outside the learned models' training conditions. It combines this fidelity with high GPU throughput, approximately constant over the tested resolution range, and favorable ensemble scaling.

This paper develops a framework for quantifying operational flexibility in radial electric distribution systems under coupled active- and reactive-load uncertainty. Flexibility is defined as the largest scaling of a prescribed uncertainty set that can be accommodated while satisfying network and actuator limits under a given control architecture. We consider a one-sided Euclidean set for robust demand-growth capability, with a direct extension to bidirectional variations. Under LinDistFlow, controllable distributed resources and feeder-head exchange with the upstream grid jointly balance active-power disturbances, while local Volt/VAR feedback regulates reactive power. The resulting affine closed-loop disturbance response admits exact second-order-cone robust counterparts. For fixed Volt/VAR gains, a convex reformulation jointly optimizes the flexibility margin and active-power participation factors, including feeder-head participation. A sparse load-to-generator feedforward map further captures communication constraints. Numerical studies on the Iowa-240 feeder demonstrate how the amount of control authority and communication structure shape the achievable flexibility margin.

This paper addresses the "trust gap" in deploying AI-enabled spacecraft autonomy through an integrated verification framework combining offline ground testing with online flight monitoring. The offline phase progresses systematically through simulation studies, processor-in-the-loop (PIL), hardware-in-the-loop (HIL), and robotic emulation testing, culminating in flight validation. The online phase employs AI watchdog systems for performance monitoring and safety guardrails for real-time constraint enforcement. Key contributions include: (1) systematic organization of verification techniques emphasizing closed-loop behavioral testing at each phase, (2) integration of AI watchdogs with runtime assurance, and (3) demonstration of the framework through a reinforcement learning-based attitude control case study. This approach integrates mature technologies to provide a viable pathway for safe deployment of AI-enabled autonomy in high-consequence space operations while addressing simulation-to-reality transfer challenges.

Controlling an agent in an environment solely perceived through onboard vision is challenging: perception yields noisy, variable-length, and partially occluded context, while onboard compute is shared between perception and control, limiting the planning horizon executable in real time. We propose VIGOR-MPC, a modular student-teacher framework, agnostic to the perception pipeline, which addresses reference tracking across navigation tasks by distilling a privileged, field-of-view-aware long-horizon MPC into a computationally lightweight short-horizon MPC whose cost parameters are determined online from vision. The teacher operates in simulation with full state knowledge and a multi-trajectory formulation that guarantees the agent can stay within its field of view. The student observes only the noisy, occluded context produced by a perception module and learns, via a multi-level recurrent mapping, to infer cost parameters that aim to recover the teacher's behavior despite missing information. We evaluate VIGOR-MPC on vision-based autonomous racing in simulation and on hardware. Using only onboard perception of the track, VIGOR-MPC generalizes to tracks unseen during training and approaches the lap times of the privileged long-horizon teacher while reducing per-step computation by over 90%.

This paper proposes a novel adaptive learning method for unknown time-varying parameters by deliberately incorporating a delay into the parameter update. Specifically, a conventional adaptive estimator is augmented with only one additional dynamic state, which estimates the parameter variation rate from the difference between current and previous parameter estimates. The method fundamentally changes the influence of time variation on estimation performance: the resulting estimation error is governed by the second derivative of the unknown parameter rather than its first derivative. Under persistent excitation, a small-gain analysis proves exponential stability of the complete estimation-error system and establishes explicit bounds for the parameter, state-prediction, and parameter-rate errors. In particular, accurate learning of linearly time-varying parameters and their unknown rates can be guaranteed with errors converging exponentially fast to zero. Numerical comparisons with conventional adaptive estimation and a recent timevarying parameter estimator demonstrate superior learning performance in the considered examples. The proposed delay-enabled adaptive estimator requires neither state derivatives nor any assumed model of parameter variation, and its implementation consists of one delayed difference and a first-order filter. These results thus provide a direct extension of adaptive learning from constant to time-varying parameters without sacrificing the simplicity of the conventional adaptive estimation mechanism.

This work proposes a multi-target tracking solution that reasons over combinations of sensor-level tracks to perform distributed multiple hypothesis tracking (MHT) in an asynchronous multi-sensor network. This approach handles clustered target tracking and allows for efficient information pedigree summarization for distributed data fusion (DDF). By decomposing the association problem into partitioned hypothesis sets and fusing across sensor track combinations, the method reduces combinatorial complexity compared to traditional MHT approaches. Evaluation on a satellite constellation-based tracking scenario demonstrates improved estimation accuracy and data association quality compared to a reference target tracker with global nearest neighbor association.

Deep reinforcement learning (DRL) policies for planetary landing and other aerospace guidance, navigation, and control tasks are commonly trained under randomized disturbances to improve robustness. However, typical stochastic perturbation sampling assumes independent variations, whereas real mission disturbances often exhibit spatial and temporal correlation and cannot be reproduced through independent random draws. This work investigates adversarial reinforcement learning to discover challenging off-nominal conditions. A secondary agent generates physically constrained perturbations that degrade the performance of a landing controller, forming an asymmetric sequential zero-sum game. Training proceeds in three stages: baseline controller learning, robustness enhancement with stochastic perturbations, and an iterative adversarial phase in which the controller and disturbance generator are alternately optimized. Preliminary experiments in a three-degree-of-freedom landing environment show that adversarial disturbances reveal vulnerabilities missed by conventional randomization and provide early indications that, with a more extensive training campaign, the approach can yield improved robustness and stronger generalization under previously unseen perturbation regimes.

In this paper, we develop a novel MIMO controller synthesis method that relies solely on frequency-domain data of discrete-time systems. The approach is based on an iterative design procedure using integral quadratic constraints (IQCs). It generalizes existing data-driven methods that approximate semi-infinite IQC conditions by enforcing them on a finite set of measured frequency points. Such approximations do not provide guarantees on the continuous frequency axis and may fail to capture inter-frequency behavior. We address this limitation by formulating a convex optimization problem that preserves robustness guarantees over the entire frequency range. In particular, the proposed method explicitly accounts for inter-frequency behavior of both plant and controller, leading to certified robust stability and performance. The effectiveness and computational efficiency of the approach are demonstrated using experimentally obtained frequency response data from industrial-grade semiconductor equipment. The synthesized controller is implemented on the physical setup, and its performance is validated in real-time experiments.

Robotic vacuum cleaners must extend their functionality within tight limits on size, cost, and durability. Deployable linkages with few actuators meet these limits, but their backdrivability makes it difficult to hold a load-bearing posture without continuous motor torque. We introduce a two-stage deployable linkage that is self-locking, in the sense used for toggle and singularity-based linkages, through its own geometry: at full deployment an internal three-link subchain approaches a collinear, near-singular configuration where the input–output transmission ratio approaches zero, so that plate loads produce a small residual back-driving torque, absorbed by drive-train friction and a lightweight magnetic detent. A single actuator performs deployment and locking, a passive chassis-mounted trigger releases the lock for retraction, and a second actuator adjusts only the deployment height. We integrate the mechanism into a commercial robotic vacuum cleaner and validate it through a 100-cycle repeatability test, a quasi-static load test in which the locked plate held 50\,N with the motor de-energized, and 140 pushing-type tidying trials on seven household objects against a commercial arm-equipped vacuum robot under a documented protocol. We derive the analytical holding condition and discuss its limits as an intermediate design point between passive compliance and articulated manipulation.

Multi-agent path finding (MAPF) solvers are evaluated on the schedules they produce, not on how well those schedules can be executed. Specifically, continuous-time optimal solvers can achieve even lower cost than their discrete counterparts by scheduling agents with near-zero temporal/spatial separation, and by exploiting graphs with higher connectedness; however, such schedules may prove hard to execute under a realistic controller. We integrate AOC-CBS, an anytime-optimal continuous-time MAPF solver, into a fleet coordination framework in which each agent tracks its timed path using model predictive control, and we evaluate the resulting system on congested MovingAI benchmark instances with 21 to 40 agents. Without intervention, only a fraction of the instances complete execution. We then apply T-robustness by adding a time-margin, i.e., a minimum temporal gap between agents at shared locations, as well as spatial inflation of the agent footprint. Increasing either margin raises execution success, for a relatively small increase in the schedule's sum-of-costs; it also improves how closely agents adhere to the schedule: when tracking each agent's position throughout execution and comparing it against the position it should occupy according to the schedule, the mean discrepancy falls significantly as the margin increases. In general, the two margins have different effects; temporal margin decreases deviation, giving the controller room to stay on schedule. Spatial margin affects deviation less and instead increases the clearance between agents, so that the same deviation no longer leads to a blocking stall. They are nonetheless substitutes rather than complements: spatial inflation is almost free in schedule cost, but its effect on execution success is small and does not compound with a temporal margin already in place.

Near-field beamfocusing confines radiated power into a localized three-dimensional focal spot, making the analytical characterization of radial and angular resolution critical for sixth-generation (6G) multi-user systems, yet existing models treat the focal resolution as a fixed geometric outcome rather than a tunable design variable. Based on the Angular Representation (ASR) approach, this paper presents a resolution framework to design contiguous focal regions across both the radial and angular domain for steered multi-user systems. By extending the definition of a tunable inter-beam overlap $\rho$, we derive closed-form expressions for the radial and angular focal boundaries and extents, alongside the steering-dependent minimum focal distance $d_{0,\text{min}}$ and maximum steering angle $\theta_{\text{max}}$. Using these boundaries, iterative synthesis algorithms are developed to construct uniform and non-uniform radial and angular beam arrangements. Finally, by analyzing both isolated and joint radial-angular configurations, we quantify the fundamental trade-offs between $\rho$, achievable user capacity, inter-user interference, and system sum-rate across varying steering angles.

A staged physics-informed neural network (PINN) is proposed for identifying the inductance, capacitance and parasitic resistances of a DC buck converter from time-domain data. Gradients are released in the order of L to C to rL to rC, a sequence derived from sensitivity analysis. The five-stage schedule recovers L and C to within 5% and the series ESR to 10%, while a three-stage variant attains the same accuracy 11% faster. An 84 factorial sweep confirms that the trained PINN remains accurate for simultaneous 8% perturbations of all four components, achieving a waveform RMSE of 2.6 x 10-2 over 5 ms-an order-of-magnitude better than a simultaneous-training baseline.

High penetration of single-phase distributed power production and frequent topology changes can significantly increase voltage unbalance in distribution networks. Reliable probabilistic power flow (PPF) assessment for the network, therefore, must account for topology-dependent power-flow equations, numerical error in the voltage unbalance factor (VUF), and finite-sample uncertainty in power measurements. In this paper, we propose Topology-Adaptive Certified Quantile Estimation (TACQE) to estimate the maximum VUF across monitored electric buses. Following a local network change, TACQE updates the cross-couplings among power-flow sequences, and certifies candidate PPF solutions against the updated nonlinear equations. The resulted power-flow state enclosures provide rigorous bounds on the maximum VUF. Furthermore, we derive independent reference-solver evaluations to calibrate discrepancies from the reference PPF solution. This leads to a uniform sampling bound that yields simultaneous upper-tail probabilities and quantiles, which provides a finite-sample guarantee for the estimated maximum-VUF distribution over an entire range of VUF thresholds. From our numerical simulations across 101 fixed-topology quantiles, it demonstrates that the designed TACQE can provide a fast candidate post-change power flow solution where it reuses most of the previous network model and modifies only the affected bus blocks.

This paper studies popularity dynamics in social networks, where users' engagement with competing channels is driven by intrinsic content quality, social influence, and popularity feedback. We introduce a stochastic model in which users interact through a directed influence network and channel exposure depends jointly on these three mechanisms. We first characterize the resulting dynamics in the absence of intrinsic quality, showing that popularity concentrates asymptotically on a single channel, leading to a winner-takes-all regime. When all channels have positive intrinsic quality, we establish ergodicity of the popularity process and characterize its long-run behavior. We further show that, when intrinsic quality is small, the system exhibits metastable behavior: it can remain for long periods near configurations dominated by a single channel before transitioning to another. These results highlight how weak quality differences can coexist with strong path dependence induced by popularity feedback. We then investigate platform-level interventions and propose controls that compensate for network-induced exposure biases and mitigate excessive concentration. Analytical results are illustrated through numerical simulations and an empirical analysis of YouTube data, whose observed popularity dynamics are consistent with the proposed stochastic framework. The results provide a mathematical framework for understanding the interplay between quality, social influence, and reinforcement in online popularity dynamics.

We present a novel class of minimum energy estimation problems for positive linear systems with linear costs subject to coupled process and measurement disturbances, where the latter is bounded by the state and the former is bounded by the noise. Through dynamic programming we show that the problem admits explicit solutions. Remarkably, the Luenberger observer structure of the optimal estimator emerges from the optimization itself, with a switching gain that inherits the sparsity of the disturbance constraints, and it is obtained from a differential equation in the finite horizon case. In the infinite horizon case, an algebraic equation characterizes the limit of the finite-horizon solution and yields a stationary estimator with a static gain. We introduce two distinct notions of detectability to characterize when the static gain stabilizes the error dynamics while preserving their positivity, and analyze under which conditions the stationary solution can be formulated as a linear program. We further establish that a disturbance perturbation of finite L1-norm yields an estimation error of finite L1-norm. The framework is illustrated on a large-scale irrigation network.

Historical blackout events demonstrate that, under complex multi-event contingencies, interconnected power systems can split into multiple areas exhibiting distinct dynamic behavior while remaining physically connected. This work provides a rationale for this phenomenon. Typical N-1 contingency analysis leads to a response dominated by a single unstable mode, supporting the two-cluster conjecture used by transient stability assessment tools such as the SIME method. However, extreme events, often involving cascading N-k contingencies, can excite multiple unstable modes. We establish a proof of concept based on linear modal expansion to show how such concurrent excitation leads to multiple coherent areas. The resulting behavior is illustrated through simulations on the standard WSCC 9-bus, IEEE 14-bus, and IEEE 39-bus systems, demonstrating the limitations of conventional two-cluster representations under extreme events.

Existing placement studies for pinching-antenna systems (PASS) usually assume that antenna movement is instantaneous and free. In practice, actuators move at finite speeds and must pause radiation while repositioning. This makes performance depend on consecutive antenna positions, turning placement into an edge-weighted longest-path problem on a temporal trellis. We also show that an obstacle blocks a wider range of antenna positions as it approaches the user, causing sudden performance drops that make multi-step lookahead essential, and that once obstacle height is included every blockage event shadows at least a fixed minimum length of waveguide. We give an exact offline solution with complexity O(TMS) and bound the per-slot loss of a W-step receding-horizon policy by O(1/W). Measurements show that the required lookahead window is set by shadow width rather than by waveguide length. Across 20 random seeds, the exact policy delivers 17.7% higher max-min throughput than per-slot optimization under blockage, and matches it when no blockers are present.

This paper studies resilient angle-constrained leader–follower formation control and localization of multi-agent systems under asynchronous denial-of-service (DoS) attacks, which block individual sensing and communication links. To preserve the well-posedness of angle-based localization, an all-or-nothing triangle integrity protocol (ANTIP) is proposed to convert edge-wise attacks into triangle-wise ones, and DoS attacks are classified into localizability-maintained and localizability-broken attacks. First, with relative position measurements over attack-free links, resilient formation tracking laws are designed for leaders with constant velocities and with time-varying velocities comprising translational, rotational, and scaling motions. These laws guarantee asymptotic tracking and finite-time recovery of tracking, respectively, when the duration of localizability-broken attacks is suitably bounded. Second, with only angle measurements and communications, a resilient simultaneous localization and formation control (RSLAF) scheme is developed. It combines a finite-time distributed observer, which estimates the exosystem matrices and states under asynchronous DoS attacks, with a perturbation-based localization law that prevents localization from getting stuck at degenerate configurations. The theoretical results are rigorously proved and illustrated by numerical simulations.

Technologies, such as co-simulation, in particular, digital twins (DTs) are used for comprehensive and reliable prototyping, verification and validation (V&V) of control systems. There, the combination of feedback loops across multiple realities (e.g., simulations, physical setups) enables various forms of experimentation with robots and autonomous systems. However, the handling of multiple realities requires a more specific support by the modelling techniques used in control engineering. To streamline the latter in the system life cycle, we propose a framework for mixing (i.e., fusing, defusing) control input and feedback signals (i.e., stimuli, responses) in simultaneously operated plants for multiple realities.

Frequency-response-function-based controller design enables synthesis directly from measured data, avoiding the need for accurate parametric plant models. This paper presents a generalized IQC-based approach for convex synthesis of structured MIMO feedback and feedforward controllers from frequency response function data. The framework supports flexible controller parametrizations, multiple performance objectives, and both singular-value and element-wise performance shaping of MIMO controllers within a unified frequency-domain formulation. To facilitate practical deployment, the synthesis approach is embedded within a bootstrapped controller design methodology that progressively increases controller flexibility while refining the performance specifications. The resulting approach is validated experimentally on the motion stage of an industrial wirebonder, where clear performance improvement is demonstrated through MIMO $\mathcal{H}_\infty$ synthesis, element-wise feedback shaping, and dynamic feedforward design. The results demonstrate that high-performance multivariable controllers can be synthesized directly from measured FRF data in a practical and systematic manner.

This paper proposes a severity-aware machine learning framework for reducing the computational burden of preventive AC security-constrained optimal power flow (PSCOPF). The central idea is to identify a compact minimum security set (MSS) containing only the non-dominated contingencies and associated post-contingency constraints that materially influence the preventive operating point while satisfying a prescribed severity tolerance. An offline procedure combines AC security assessment, severity-based ranking, non-dominated screening, and iterative reduced PSCOPF. After each reduced PSCOPF solution, exhaustive security assessment over all credible contingencies identifies additional contingency–constraint pairs whose residual severity exceeds the specified threshold. Severity is characterized using the normalized number, accumulated magnitude, maximum magnitude, and average magnitude of post-contingency violations. The resulting operating-point-dependent MSSs are used as labels for a supervised multi-label learning model that predicts the security elements required to construct the reduced PSCOPF directly from the pre-contingency state. The proposed framework therefore focuses the optimization on the contingencies and constraints that affect preventive control decisions, reducing problem size and computation time while explicitly controlling residual post-contingency severity. Results on the IEEE 60-bus and 118-bus systems, the latter having an approximately seven-times-larger PSCOPF formulation, demonstrate substantial computational reductions with limited impact on the preventive operating solution.

Supermartingale certificates formally guarantee temporal properties of discrete and continuous-time stochastic dynamical systems. However, these guarantees rely on a mathematical model of the world, leaving a sim-to-real gap that can invalidate them for the deployed system. We formulate the runtime monitoring problem for supermartingale certificates, in which a monitor observes a single system trajectory and raises an alarm when there is sufficient statistical evidence that the supermartingale certificate has been violated during execution. We present an algorithm to automatically construct a monitor using betting-based sequential tests that bound the probability of ever raising a false alarm at a user-specified level. Within this betting framework, we formulate strategies that allow the monitor to exploit spatial regularity to improve detection power while preserving this statistical guarantee. We demonstrate the efficacy of our approach on supermartingale certificate benchmarks from the literature.

The rapid growth of AI data centers introduces large, workload-dependent loads whose protection and reconnection behavior can affect power-system transient stability. Calibrating dynamic models of such loads is challenging because treating all associated elements as freely estimable may lead to weakly identified or physically confounded parameterizations. This paper proposes an identifiability-guided calibration framework for a composite load model with separate IT, cooling, and auxiliary branches. A nuisance-projected Fisher-information criterion characterizes the local identifiability of the cooling-motor composition, while a workload non-equivalence result shows that voltage-only recalibration cannot reproduce workload-dependent responses. Guided by these results, a grey-box estimator trained with branch-level supervision learns the workload-to-power relation and the cooling composition, while retaining a prescribed UPS protection law and an externally anchored reconnect-ramp duration. The learned workload relation is then distilled into a closed form for transient-stability simulation. Evaluation using 300,000 MIT Supercloud GPU power-utilization samples and 12,600 IEEE 39-bus fault scenarios yields an aggregate active-power $R^2$ of 0.948, compared with 0.856 for an LSTM baseline. Removing branch-level supervision preserves relatively high aggregate accuracy but substantially degrades cooling-component recovery. Under unseen deep voltage sags, the proposed model achieves a transient $R^2$ of 0.750, compared with 0.033 for the LSTM. Closed-loop simulations further show that cooling composition and protection behavior can affect retained-load states and voltage responses near protection thresholds.

In this paper, we investigate a wireless communication network supported by multiple uncrewed aerial vehicles (UAVs). To ensure reliable connectivity, UAVs are desired to approach the boundaries of designated no-fly zones (NFZs) without violating them when mobile users (MUs) enter NFZs. To support heterogeneous communication services, MUs are assigned different priority levels based on their individual communication types. Considering the practical constraints arising from user mobility, heterogeneous communication requirements, and NFZs, the objective is to maximize the weighted sum of MU communication rates, where weights correspond to MU priorities. This is achieved by jointly optimizing trajectory planning, UAV transmission power control, and the binary association factor between UAVs and MUs. To address this challenging optimization problem under dynamic constraints, we develop a soft actor-critic algorithm with prioritized experience replay, which learns an adaptive control policy. Numerical results show that the proposed algorithm outperforms benchmark schemes in terms of convergence speed and stability. Moreover, it effectively meets diverse communication requirements, satisfies UAV energy constraints, and ensures reliable operation under NFZ constraints.

In this paper, we develop a Koopman operator framework for modeling and control of traffic flow governed by the Lighthill–Whitham–Richards (LWR) conservation law. We show that the nonlinear dynamics induced by the Greenshields flow–density relation naturally motivate a dictionary of polynomial observables, while the controlled dynamics become bilinear in the lifted Koopman coordinates. A reduced-order Koopman model is identified from spatio-temporal traffic-density snapshots and used as the prediction model in a model predictive control (MPC) framework for ramp metering. The resulting Koopman-MPC is compared with two predictive control baselines that use, respectively, a fixed linearized LWR model and the nonlinear finite-volume LWR model. In a one-hour freeway experiment, the Koopman-based controller maintains the traffic density below the critical Greenshields value and closely reproduces the behavior of the nonlinear PDE-based MPC, with a maximum-density difference below 1%. The Koopman predictor also reduces the mean online model-evaluation cost by approximately one order of magnitude relative to the nonlinear PDE predictor. In contrast, the linearized LWR predictor is computationally cheaper but exhibits substantially larger prediction errors and yields a more conservative metering policy. These results demonstrate that the analytical structure of the nonlinear traffic conservation law can be exploited to construct an interpretable data-driven surrogate for efficient traffic prediction and control

This paper presents a comprehensive comparison of four representative reinforcement learning (RL) methods in autonomous vehicle (AV) decision-making, including: Fitted Value Iteration (FVI), Rollout Classification Policy Iteration (RCPI), Deep Q-Network (DQN), and a discrete-action adaptation of Deep Deterministic Policy Gradient (D-DDPG). The four methods are applied to the same AV decision-making problem at an unsignalized intersection and are compared in terms of optimality and safety, training stability, and training and decision cost. RCPI is the closest to a reference solution and has the most stable training process. However, it is the least data-efficient. D-DDPG reaches the second-smallest gap to a reference solution with the highest data efficiency, but has the least training stability. DQN is the most stable model-free method but has the highest collision rate. FVI achieves a lower collision rate than DQN, but is less data-efficient and less stable. Based on these results, we provide suggestions for selecting a method according to whether a model is available, the budget for training, and whether stable training is required.

We provide a distributed online algorithm for multi-agent submodular coordination in bandit environments, i.e., environments whose evolution is unknown a priori and may even adapt to the agents' actions. We are motivated by multi-robot tasks such as target tracking, where the optimal actions change over time and the agents must track them rather than a single fixed optimum. To address such problems, we provide the Optimistic Distributed Online Greedy (O-DOG) algorithm, that leverages predictions about the environment's behavior and improves regret guarantees. The agents select actions simultaneously over arbitrary, possibly time-varying, network topologies, and each agent acts optimistically, i.e., leveraging predictions of its upcoming rewards, e.g., from forecasts of the targets' motion and of its neighbors' actions. We bound O-DOG's tracking regret in terms of the agents' prediction error and the bound never exceeds its prediction-free counterpart. Particularly, it is sublinear even when the optimal action changes at every time step, provided the predictions are accurate enough. We further quantify the suboptimality due to decentralization as a function of the network structure, and show that accurate predictions accelerate convergence. We validate O-DOG in simulated multi-robot target tracking.

In this paper, we present a novel multiscale formulation for the Optimal Transport (OT) of large swarms. The scheme is divided in two levels, a top-level OT formulation with a terminal penalty cost to handle time-varying densities, and a low-level, Model Predictive Controller (MPC) that allows agents to track the nominal velocity under control affine dynamics and rejects external disturbances. We show that the terminal-penalty dynamic formulation reduces to a regularized static dual, which agents solve in a distributed way to compute the nominal velocity.We also prove distributional input-to-state stability in the $1$-Wasserstein metric of the density flow with respect to the mismatch that the low-level and the target motion introduce, so that the tracking error only perturbs the macroscopic flow by a bounded, quantifiable amount. Finally, we demonstrate via simulations the effectiveness of the proposed method handling time-varying targets and state perturbations.

The rapid proliferation of cloud computing and artificial intelligence workloads is fundamentally reshaping the magnitude, spatial concentration, and electrical characteristics of data center power demand. Data centers differ from conventional commercial loads because they combine high rack densities, converter-dominated interfaces, thermally coupled cooling, uninterruptible power supplies, programmable workloads, and increasingly complex behind-the-meter generation and storage within a single facility. This review examines their grid integration from utility and sustainable-energy perspectives across resource planning, large-load interconnection, distribution feeders, market operation, real-time dynamics, power quality, and asset management. Particular attention is given to distribution-level issues that receive limited treatment in broader energy reviews: bidirectional operation on renewable-rich feeders, harmonic propagation and resonance, ride-through and reconnection, transformer derating, cable ampacity, neutral-conductor heating, protection, and measurement. Static, composite, physics-based dynamic, calibrated, impedance, electromagnetic transient, and hybrid load models are compared according to study purpose and data requirements, and utility coordination platforms, uninterruptible power supply and battery control, workload flexibility, renewable co-location, cogeneration, fuel cells, thermal storage, and clean firm supply are assessed. The central finding is that requested peak demand and annual energy matching alone cannot establish reliable or sustainable integration. Credible assessment must consider hourly and locational supply, network deliverability, marginal emissions, lifecycle and water impacts, backup obligations, rebound, asset health, and verified operating capability.