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arXiv2026

Toward Autonomous GNSS Networking: Low-Complexity Onboard Routing and Topology-Routing Co-Construction

Inter-satellite links constitute a critical enabler for the autonomous operation of global navigation satellite systems (GNSSs).Nevertheless, onboard routing still poses substantial challenges, stemming from the frequent topology variations of GNSS constellations and the constraint of limited onboard computational resources for routing calculation. This paper develops a routing and topology-control framework tailored to the time-slotted structure of GNSS inter-satellite networks. By exploiting the fact that each satellite communicates with at most one scheduled peer in a slot, the proposed routing method propagates future delivery information backward over the slot sequence and directly determines whether data should be stored or forwarded. This structure avoids massive end-to-end path searches and enables topology generation and routing computation to proceed concurrently. The same delivery information is further introduced into topology construction so that link scheduling can directly account for end-to-end communication performance. A master-satellite autonomous orbit-determination scenario is used to demonstrate this topology--routing co-construction mechanism. In a 24-h, 30-satellite BeiDou scenario, the proposed routing method achieves routing performance comparable to Contact Graph Routing while reducing routing computation time by approximately 1500 times. The co-constructed topology further improves all-to-all communication and both master-related traffic directions while preserving strong inter-satellite ranging performance. These results fill a critical gap between autonomous topology planning and autonomous routing computation, supporting more complete onboard autonomous networking for future GNSSs.

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arXiv2026

Observer-Based Robust Control for an Aerial Manipulator System under Unknown External Disturbances

This paper addresses the mathematical modeling and control of an aerial manipulator system comprising a quadrotor as the uncrewed aerial vehicle and a robotic arm as the manipulator. The dynamic model is established by identifying the overall center-of-mass velocity and the system's orientation as constraints, yielding a simplified, two-decoupled subsystems: a locked (overall translation) subsystem and a shape-space (actuation or overall rotation) subsystem, both subjected to external disturbances. Since the system is mechanically coupled, a critical challenge arises where constant bounded disturbances in the first subsystem manifest as time-varying, state-dependent disturbances in the second subsystem. Given that the quadrotor is inherently unstable, the movement of the robotic manipulator (RM) during flight can further jeopardize the stability of the entire QRM system if these disturbances and coupling effects are not effectively managed. To address this, we present a continuous nonlinear disturbance observer-based feedback control law, which enables the independent control of each subsystem while systematically eliminating cross-coupling effects. The efficacy of the proposed controller is validated through multiple simulations emulating practical operating conditions, thereby substantiating its real-world applicability and highlighting the core contributions of this work.

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arXiv2026

Timescale-aware surrogate-assisted multi-objective optimization of battery cell design for energy density, fast charging, and degradation

Battery cell design must balance energy density, fast charging, and degradation, yet these metrics evolve over different time scales and are costly to optimize jointly. We develop a timescale-aware surrogate-assisted framework that evaluates beginning-of-life volumetric energy density and 10--80% charging time together with state-of-health (SOH) loss over 200 cycles. Physics-based simulations of 1501 cell designs generate 1427 quality-controlled samples across 12 manufacturing-relevant parameters. Objective-specific surrogates support total-order Sobol analysis, which reveals distinct parameter rankings across the three metrics. A cross-objective rank-union strategy then retains variables influential to at least one objective before evolutionary Pareto optimization. Re-evaluation of the optimized candidates using the original physics-based models confirms designs that outperform the reference cell in all three metrics. Among these jointly improving candidates, the objective-wise best solutions, attained by different designs, can reduce SOH loss by 99.31%, increase volumetric energy density by 12.49%, and shorten charging time by 28.73%. The framework makes Pareto exploration across disparate electrochemical timescales computationally tractable, thereby enabling systematic multi-objective optimization of battery cell design.

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arXiv2026

Decentralised Plug-and-Play Stability Conditions for AC Grids-Part I

The rise of renewable generation in AC power systems calls for small-signal stability assessment methods that do not rely on centralised, system-wide models. In this two-part paper, we present a decentralised stability framework based on frequency-domain conditions imposed on local subsystems (many admitting equivalent graphical interpretations) which collectively imply the stability of the entire grid. The framework generalises many previously reported results, with added flexibility and plug-and-play compatibility provided by the additional degrees of freedom of the imposed conditions, the ability to mix conditions across frequency ranges, conditions on extended subsystems that couple buses to their connecting lines and loads, and a method to incorporate unstable subsystems. Detailed, heterogeneous device and line models are accommodated in the frequency domain using impedance, admittance, and power-flow models, making the framework suitable as a foundation for grid codes that enable plug-and-play functionality in inverter-dominated grids. Part I focuses on stable impedance and admittance representations and Part II extends the results to unstable subsystems, which are shown to be fundamentally present in many grid models. The results of Part I are validated on a modified 9-bus case study, demonstrating the reduced conservatism and significance of the proposed approach.

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arXiv2026

Decentralised Plug-and-Play Stability Conditions for AC Grids-Part II: Unstable Subsystems

Part I of this paper presented a decentralised framework for certifying small-signal stability in AC grids using frequency-domain quadratic constraints on individual grid subsystems. Part II extends the framework to a broader class of networks that contain unstable subsystems. In particular, we show that such unstable subsystems arise in many common scenarios, even when the aggregate system is stable and well behaved. Such subsystems must be stabilised by the closed-loop network interconnection, which complicates decentralised stability analysis. A stable hybrid representation resembling the power (PQ) model at low frequencies and the impedance (IV) model at high frequencies is then defined, to which the stability framework of Part I can be applied, allowing plug-and-play compatible conditions to be formulated. Furthermore, we show that at low frequencies, the characteristic loci in the Nyquist plot of the return ratio in this hybrid representation decouple into unbounded and bounded branches along the classical $P$-$δ$ / $Q$-$V$ separation, and give sufficient conditions on each branch for ensuring system stability. The results are validated on the Kundur two-area system, where stability is certified, with a decentralised, plug-and-play compatible grid code covering the frequency ranges in which electromagnetic interactions arise.

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arXiv2026

Stochastic Model Predictive Control under AC Power-Flow Constraints Using Generative Learning

This paper proposes an end-to-end generative framework for efficiently solving multi-period and multi-scenario stochastic model predictive control (SMPC) problems under nonlinear AC power-flow constraints. Conventional deterministic neural surrogates rely on a single-shot prediction, which makes reliable feasibility difficult to achieve because one dispatch trajectory must simultaneously satisfy nonlinear constraints across all scenarios and time periods. To address this limitation, a conditional stochastic neural generator (CSNG) is developed to produce multiple candidate dispatch trajectories for each uncertainty instance, enabling the recovery of a feasible and economical solution through candidate selection. A feasibility-aware self-supervised distribution-shaping scheme is further introduced to promote constraint satisfaction, candidate diversity, and operating economy without requiring computationally expensive SMPC solution labels, while mitigating candidate collapse in generative ACOPF learning. To support efficient end-to-end training, a constraint-aware differentiable architecture is introduced. It employs a projection mechanism to exactly enforce box and ramping constraints while preserving informative gradients near active bounds, together with a differentiable equality-completion surrogate for efficient AC power-flow reconstruction. Case studies on the IEEE 14- and 118-bus systems demonstrate $100\%$ feasibility, optimality gaps below $2\%$, and computational efficiency suitable for intraday dispatch. The implementation is publicly available at https://github.com/JieZhu6/Generative_SMPC.

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arXiv2026

Decentralized Safe Multi-Agent Reinforcement Learning via Predictive Shielding

Environments are increasingly populated by multiple robots performing independent tasks with limited prior knowledge of each other. Deploying such multi-agent systems presents significant challenges. Specifically, shifts in deployment states compared to training data can lead to poor policy performance and compromised safety. While safety shields exist to mitigate these risks, they are typically reactive, which degrades performance near unseen obstacles,and centralized, limiting their scalability. To address this, we propose a decentralized framework that integrates predictive shielding with model-based finite horizon Q-learning. This approach allows agents to safely adapt their pre-trained policies during deployment. Furthermore, to mitigate livelocks in symmetric scenarios, we introduce a communication- free protocol for conflict resolution

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arXiv2026

Data-driven estimation of design heating loads for HVAC equipment sizing

Oversized heating and cooling equipment can unnecessarily increase up-front costs, energy costs, pollutant emissions, and strain on electrical infrastructure. This paper develops two data-driven methods for estimating heating loads at design conditions to improve equipment sizing. One method uses smart thermostat data; the other uses utility bills. We test the methods on a dataset that we gathered from 74 detached single-family houses in five United States climate zones. The dataset includes smart thermostat time-series data, monthly utility bills, weather data, existing equipment specifications, and Manual J design load calculations (the United States industry standard) purchased from practitioners. The two methods have strong goodness-of-fit statistics individually and show fair agreement with each other. On average over the 74 houses, existing heating equipment is 2.75 (2.5th to 97.5th empirical percentile: 1.34-6.52) times larger than the thermostat-method estimate and 2.96 (1.36-7.76 times larger than the bill-method estimate. As implemented by practitioners, the Manual J estimate is 2.26 (0.87-5.21) times larger on average than the thermostat-method estimate and 2.35 (1.02-5.28) times larger than the bill-method estimate. We discuss prospects for implementing the data-driven methods at scale and for incorporating data-driven sizing into industry standards.

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arXiv2026

A unified power-grid representation for reuse across network structures and computational tasks

Data-driven power-system models are typically developed for specific grids and computational tasks, but their performance can deteriorate markedly or even fail when network structures or analytical objectives change. This paper develops a unified grid representation that separates physical-grid description from downstream computation. A self-supervised encoder represents each grid as a variable number of fixed-dimensional node, branch and global vectors under a common latent description. Although pretrained only on systems with at most 270 buses, the frozen encoder transfers without adaptation to a 70,000-bus network, more than 250 times larger in bus count, while preserving 0.7919 bus-correspondence accuracy. The same representation supports four independently trained downstream tasks: power-flow calculation, reactive-power adjustment, operating-condition generation and transient-stability assessment. On the previously unseen 70,000-bus grid, power-flow calculation achieves mean errors of 0.108$^{\circ}$ in phase angle and 3.8$\times$10\textsuperscript{-6} p.u. in voltage magnitude. Reactive-power adjustment achieved a 75\% PSASP-verified correction rate on the previously unseen 70,000-bus grid, while operating-condition generation delivered at least one PSASP-verified feasible state for 82.2\% of requests on previously unseen 400--1,000-bus grid families. For transient-stability assessment, the frozen encoder achieves accuracy within 0.8 percentage points of full encoder adaptation under matched training conditions. These results demonstrate that a unified power-grid representation can be learned once and reused unchanged across structurally different power systems and heterogeneous computational tasks.

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arXiv2026

Emergent Charging Coordination in Electric Delivery Fleets

In electric delivery fleets, mid-shift charging is non-trivial: each vehicle must decide when, where and how much to charge to finish on time with battery above a safety floor. The choices are coupled: queues build where too many vehicles pick the same station. Prior work resolves this coupling with central dispatching, precomputed schedules or reservations, machinery that charging infrastructure rarely supports. Instead, we use a family of learning agents under purely local control: every vehicle runs the same policy, deciding alone from its time budgets and broadcast station occupancies, leading to emergent coordination without central control or messaging. We validate this paradigm in simulation on real OpenStreetMap networks of twenty cities, each with a frozen scenario calibrated by an omniscient Oracle (99.5% of shifts completed on time), whereas a naive greedy rule (nearest station on low battery) completes just 73%. Agents trained with neuroevolution (NEAT) and policy gradients (PPO) on four cities and deployed zero-shot across all twenty, sixteen never seen in training, complete 96.8% and 98.6% of shifts, with the policy-gradient controllers proving more robust when demand or vehicle characteristics drift beyond the trained regime. In contrast, tuned threshold heuristics that read vehicle urgency alone fall short in contended cities (~80%). Through training, these learning agents rediscover partial charging and short opportunistic sessions, and route around busy stations, cutting per-session queue waits from about 45 minutes to under 2. In summary, this coordination paradigm balances local urgency against public occupancy, reaching near-Oracle performance at minimal implementation cost.

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arXiv2026

Online Constrained Control of Storage Systems via Simplex Disturbance-Action Policies

We study online control of a scalar storage system with nonnegative adversarial resource arrivals and state-dependent action constraints. The adversarial, time-varying cost depends on both state and action. At each time, the controller selects a feasible action before the current resource arrival and cost are revealed. To handle the coupling between feasibility and online learning, we introduce Simplex Disturbance-Action Control (SDAC), whose policies have $H$ nonnegative parameters summing to at most one. Every fixed SDAC policy is feasible by construction. Our online SDAC controller updates these parameters using entropic online mirror descent while preserving feasibility along the time-varying trajectory. We prove a regret bound of $O\!\left(\sqrt{T\log(H+1)}\right)$ relative to the best fixed SDAC policy and a minimax lower bound showing that the dependence on $T$ is tight. We also introduce infinite-memory SDAC policies, which include every feasible fixed-fraction policy. SDAC policies approximate this class with an error that decreases geometrically with $H$. For a suitable choice $H=Θ(\log T)$, the resulting regret against infinite-memory SDAC policies is $O\!\left(\sqrt{T\log\log T}\right)$. The framework applies to energy-harvesting batteries and other storage-constrained resource systems.

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arXiv2026

Distributed Droop-Free Control of Grid-Forming Inverters with Dynamic Line and Load Modeling

Droop-free distributed control has emerged as a promising alternative to conventional linear droop control for coordinating inverter-based resources in AC microgrids. However, existing droop-free methods typically rely on quasi-steady state network models that neglect fast electromagnetic transients and assume a decoupled dependence of active and reactive power on frequency and voltage, respectively. In this paper, we propose a transient-aware droop-free distributed primary/secondary control framework for grid-forming (GFM) inverters. The control objective is to achieve proportional active and reactive power sharing, frequency regulation, and voltage regulation within prescribed bounds. The proposed approach incorporates dynamic models of network lines and loads into the control design, enabling accurate representation of transient behavior while capturing the coupled nature of power flow interactions. A gradient-based distributed control update is derived from an optimization formulation, in which voltage magnitude constraints are enforced through a projection operator. To validate the performance of the proposed control, real-time simulation studies are conducted on a four-inverter microgrid under load changes in both strong and weak grid conditions. The results demonstrate that the proposed method improves transient performance, particularly in weak grids, while preserving the same steady state operating conditions as existing QSS-based droop-free approaches.

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arXiv2026

Configuration-Constrained Tube MPC for Difference-of-Convex Nonlinear Systems

This paper develops a convex tube model predictive control formulation for constrained nonlinear systems. We consider dynamics described by a discrete-time state-space model with parametric and additive uncertainty that admits a difference-of-convex decomposition. Convex directional bounds of the nonlinear dynamics are combined with configuration-constrained polytopic tubes, whose predefined combinatorial structure yields an affine parameterization of their vertices. The resulting finite-dimensional sufficient conditions certify robust one-step tube propagation under parametric uncertainty and additive disturbances, while allowing the tube geometry and an associated vertex control law to be optimized jointly in a single convex program. An implicit terminal condition guarantees recursive feasibility and convergence of the predicted tube to a target robust control invariant set. Numerical results illustrate the closed-loop properties and the trade-off between geometric flexibility and computational complexity.

math.OC↗

arXiv2026

Trajectory Optimization via Schrödinger Bridge Sampling

We take a new look at the relation between finite-horizon trajectory optimization and Schrödinger bridge sampling. Viewed as inference, KL-regularized trajectory optimization is solved by sampling from a Gibbs--Boltzmann distribution whose energy is the trajectory cost, and the adjoint Schrödinger bridge sampler (ASBS) is a simulation-free diffusion sampler designed for exactly such unnormalized targets. Hard equality path and terminal constraints, by contrast, confine the admissible decision variables to a measure-zero feasibility manifold, on which the target must be redefined intrinsically. In particular: $(a)$ we analyze two complementary parametrizations; a rollout parametrization, in which the dynamics are eliminated and only the remaining constraints shape the manifold, and a double-shooting parametrization, in which states and controls are sampled jointly and the dynamics themselves become part of the manifold; $(b)$ we establish regularity conditions under which both admissible sets are smooth embedded manifolds; $(c)$ under compactness and path-connectedness assumptions, we sample from the resulting intrinsic Gibbs measures via Riemannian ASBS, treating strict inequalities through exponential slack variables. Experiments, including contact-rich locomotion and manipulation, demonstrate the effectiveness of both regimes.

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arXiv2026

Koopman-Based Model Predictive Control for Simultaneous State of Charge and Temperature Balancing of Lithium-Ion Cells

Cell-to-cell electrical and thermal variations produce nonuniform state of charge (SoC) and temperature distributions in lithium-ion battery packs. This paper proposes a Koopman-operator-based framework for simultaneous SoC and temperature balancing of series-connected cells. Cell-specific Koopman predictors identified using extended dynamic mode decomposition are assembled into neighboring-cell error dynamics that retain heterogeneity and thermal-disturbance effects. A Tikhonov-regularized feedforward controller provides numerically robust compensation without excessive current amplification, while constrained Koopman-based model predictive control (KMPC) regulates the residual errors through a convex quadratic program. Three-cell simulations validate the controller under two consecutive Urban Dynamometer Driving Schedule cycles. KMPC achieves balancing performance comparable to nonlinear model predictive control (NMPC). Relative to the uncontrolled case, KMPC reduces the RMSEs of the neighboring-cell SoC and core temperature differences by 66-81% and 57-76%, respectively, while reducing the computation time per optimization from 0.0259s for NMPC to 0.0028s for KMPC.

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arXiv2026

Bi-ZOL: Bilevel Zeroth-Order Learning with Nonsmooth Responses

This paper studies lower-level-constrained bilevel optimization in a response-oracle setting, where lower-level model information is unavailable and the induced response mapping is locally Lipschitz but potentially nonsmooth. In this setting, the classical response Jacobian and reduced hypergradient may fail to exist. We propose Bilevel Zeroth-Order Learning (Bi-ZOL), a structure-guided zeroth-order method for finding stationary points of the nonsmooth reduced problem. Instead of estimating the gradient of a fully smoothed reduced hyperobjective, Bi-ZOL separates the bilevel chain-rule structure: it keeps the exact upper-level partial gradients at the queried response and uses zeroth-order sampling only to estimate the response Jacobian. This construction yields an approximate hypergradient that is more directly aligned with the Clarke chain-rule subdifferential. We show that the Bi-ZOL direction admits a partial-smoothing interpretation, quantify its pointwise structural bias, and prove finite-time convergence to a $(δ,ε)$-Bi-ZOL Frank--Wolfe stationary point. The bias is $O(δ)$ for piecewise $C^{1,1}$ responses under local regularity and vanishes for piecewise affine responses on active-cell neighborhoods. Experiments on incentive-based tracking problems show that Bi-ZOL achieves smaller stationarity gaps and lower hyperobjective values than vanilla zeroth-order smoothing under comparable response-oracle budgets.

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arXiv2026

Blockage-Aware Multi-RIS Sensing and Optimization for mmWave Smart Radio Environments

Millimeter-wave (mmWave) multi-user MIMO systems are highly vulnerable to blockage, and reconfigurable intelligent surfaces (RIS) have been proposed as a remedy. However, RIS links may themselves be blocked, while most prior works assume ideal RIS availability. We propose an end-to-end blockage-aware multi-RIS weighted sum-rate (WSR) optimization framework. The BS transmits short per-RIS indexed synchronization signals, enabling each user to identify blocked panels through a simple energy detection test. Based on the detected feasible sets, we jointly optimize the BS precoder and RIS phases via a Closed-form Riemannian Phase Alignment (CRPA) algorithm. CRPA provides unit-modulus-preserving closed-form updates, requiring no projection or line search, and ensures monotone ascent. Simulations validate reliable blockage detection and notable WSR and convergence gains over existing baselines.

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arXiv2026

Optimal Scheduling of Electricity and Water in Renewable-Colocated Desalination Plants

We develop a mathematical framework for the optimal scheduling of flexible water desalination plants (WDPs) as hybrid generator-load resources. WDPs integrate thermal generation, membrane-based controllable loads, and renewable energy sources, offering unique operational flexibility for power system operations. They can simultaneously participate in two markets: selling desalinated water to a water utility, and bidirectionally transacting electricity with the grid based on their net electricity demand. We formulate the scheduling decision problem of a profit-maximizing WDP, capturing operational, technological, and market-based coupling between water and electricity flows. The threshold-based structure we derive provides computationally tractable coordination suitable for large-scale deployment, offering operational and economical insights into how thermal and membrane-based desalination colocated with renewables complementarily provide continuous bidirectional flexibility. The thresholds are analytically characterized in near closed form as explicit functions of technology and tariff parameters. We examine how small changes in the exogenous tariff and technology parameters affect the WDP's profit. Extensive simulations illustrate the optimal WDP's operation, profit, and water-electricity exchange, demonstrating significant improvements relative to benchmark algorithms.

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