Systems and Control
A Decoupled Approach for Modeling N-k Interdiction with Induced Failures in Electric Power Grids
Andrew Chio, Kaarthik Sundar, Russell Bent et al. (+1 more)
The electric power grid is a critical infrastructure system subject to different types of failures which can have devastating impacts on society. One framework to study the resilience of the grid is the N-k interdiction model, which introduces an attacker and defender model to identify grid vulnerabilities. However, traditional N-k interdiction models characterize the power grid with steady-state assumptions. One key aspect missing from this analysis is the behavior of the grid as it evolves from the pre-failure grid state and post-failure grid state. During such a transition period, automated protection systems, transient instabilities, and other phenomena can trigger cascading failures that can occur before grid operators can respond. To bridge this gap, this paper first introduces the generalized induced failure N-k (N-k.k') problem, where an initial set of k failures induces an additional set of k' failures. In this context, we propose a disjoint optimization approach that decouples the computation of cascading failures from the interdiction model. This system response model, furthermore, is treated as a black box module without compromising the underlying performance guarantees of the interdiction model. Extensive experiments on several
benchmark power grid case studies showcase the efficacy and scalability of the approach.
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All authors Andrew Chio, Kaarthik Sundar, Russell Bent, Nalini Venkatasubramanian
Systems and Control · Cross-listed from cs.RO
Full-Order Safety for Nonlinear Reduced-Order MPC
Luis Pabon, Johannes Köhler, Revant Kasichainula et al. (+1 more)
Reduced-order models (ROMs) make model predictive control (MPC) of high-dimensional nonlinear systems tractable, but the resulting modeling error can lead to constraint violations on the real system. We introduce CoRE-MPC, a robust MPC formulation that plans with a nonlinear ROM and guarantees recursive feasibility and constraint satisfaction for the full-order system. It can be applied with a broad class of nonlinear model reduction techniques and the resulting MPC does not require evaluating the full-order model online, thus ensuring computational efficiency. The key insight is in how the ROM affects the dynamics of the prediction error. We account for this with two error radii that are propagated alongside the ROM and provide a guaranteed bound on the prediction error. The approach is validated in simulation on an 80-state oscillator chain, a Burgers equation, and a 9,768-state finite-element soft
robot, where a two-state ROM with data-driven bounds keeps all 1,628 nodes clear of a wall at a median solve time below 1 ms for a 20 ms control period. Across these examples, CoRE-MPC is 13 to 61 times faster than full-order robust MPC at only 27 to 35% higher closed-loop cost, whereas existing certified schemes are either infeasible or at least twice as conservative. Code: [released upon publication].
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All authors Luis Pabon, Johannes Köhler, Revant Kasichainula, Marco Pavone
Systems and Control
Measurements and Analysis of Distortion in Inertial Force Exciters for Flat-Panel Loudspeakers
Stephen F. Roessner and Michael C. Heilemann
Flat-panel loudspeakers are typically driven by dynamic exciters that employ a voice coil coupled to a reaction mass to exert force on the panel. Unlike conventional loudspeakers, where the cone is relatively light weight, heavy panels may require significant exertion from the driver to achieve sound pressure levels required of consumer grade loudspeakers. Overexertion can cause harmonic distortion and reduced sound quality, especially in drivers where the voice coil is not centered in the
magnetic field. There are many different exciters available commercially with different power ratings and price points. The frequency response, linearity, total-harmonic distortion (THD), intermodulation distortion (IMD), and force vs. displacement were measured for three types of exciters to determine the consistency of manufacturing and use in panel loudspeakers. These exciters were found to have high variability in all metrics among units of the same type. Distortion metrics such as IMD may be reduced by running impedance sweeps to match exciters with similar response characteristics in multi-actuator panels.
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Systems and Control · Cross-listed from cs.RO
Learning in Orbit: Onboard Training and Closed-Loop Attitude Control with a Thermal Segmentation Model on a Spacecraft
Elena Sorina Lupu, Hannah Grauer, Phaedrus Leeds et al. (+9 more)
Deep space missions increasingly require spacecraft capable of autonomously operating in environments with communication delays and bandwidth limitations. A key enabler of such autonomy is the ability to run learning-based
algorithms directly onboard. In this work, we present a flight deployment on a 6U CubeSat that demonstrates (1) onboard training and inference of a thermal segmentation model on an NVIDIA Jetson TX2 NX GPU in space and (2) closed-loop attitude control using the segmentation model on a real-time, thermal image feed to track the Moon. In addition to this demonstration, we implement ROS 2 Foxy as a data transport middleware and integrate an open-source file system that provides strong data integrity guarantees and self-healing features. Over the course of approximately one year, we performed 12 experiments on the spacecraft, advancing the state of the art in autonomous spacecraft systems by showing how modern learning-based pipelines, commercially available hardware, and open-source software can operate reliably under the compute and environmental constraints of Low Earth Orbit (LEO).
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All authors Elena Sorina Lupu, Hannah Grauer, Phaedrus Leeds, Nikhil Ranganathan, Connor Lee, Sulekha Kishore, Cailyn Smith, Shon Cortes, Brian Hardy, Benjamen Bycroft, Darren W. Rowen, Soon-Jo Chung
Systems and Control
Global Parameter Identification and Uncertainty Quantification of reduced-order ADM1 using Maximum Likelihood Estimation and Bayesian Inference
Simon Hellmann, Leander Lerch, Terrance Wilms et al. (+6 more)
Anaerobic digestion (AD) models can be challenging to calibrate against real data due to limited practical identifiability, data scarcity, and model complexity. Weighted least squares objective functions and gradient-based solvers using finite differences are often used in literature, but result in suboptimal parameter estimates and large uncertainty bands. This paper identifies the parameters of a reduced-order ADM1 by comparing the frameworks of maximum likelihood estimation (MLE) and
Bayesian inference (BI), and quantifies uncertainties of parameters and outputs. Multirate experimental data are taken from a lab-scale AD reactor fed with agricultural substrates and operated at organic loading rates of 3$\,$g VS $\mathrm{m^{-3}d^{-1}}$. Global optima are found via global and gradient-based local search
algorithms using forward sensitivities. Enhanced scatter search provides the best global search results. These are refined by the Gauss-Newton
algorithm as the best local search, which delivers $R^2$ values of 0.95 for acetic acid and 0.77 for biogas production. BI yields substantially narrower uncertainty bands than MLE and avoids the need for a global search by incorporating prior knowledge about parameter distributions. The search space is captured efficiently by log10 scaling of parameters. Algorithmic details on its implications are provided. Despite a high-resolution lab-scale
dataset, the ADM1-specific inhibition parameter $K_\mathrm{I,nh3}$ is practically not identifiable with MLE, and shows a low output sensitivity, whereas a parameter for the effective ion concentration shows superb practical identifiabilty at high confidence with both MLE and BI. The present work lays a scalable base for calibration of nonlinear AD models by providing reliable simulations. It thus advances model-based monitoring and control applications of multirate processes such as agricultural AD plants.
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All authors Simon Hellmann, Leander Lerch, Terrance Wilms, Moritz Reumschüssel, Alberto Meola, Félix Delory, Steffi Knorn, Stefan Streif, Sören Weinrich
Systems and Control · Cross-listed from cs.GR
DiffGIPC: Differentiable Mixed-Coordinate Contact Dynamics on GPU
Kemeng Huang, Bowen Ren, Xinyu Lu et al. (+2 more)
Contact-rich inverse problems require efficient gradients and reliable enforcement of non-penetration. Existing differentiable methods provide fast simulation and rigid-deformable coupling, but contact treatments based on penalties or velocity-level constraints can permit penetration or positional drift. Incremental Potential Contact (IPC) maintains non-penetration through
geometric barriers and {continuous collision detection (CCD)}. However, repeated forward and backward evaluations remain costly in full-space finite-element (FEM) implementations, particularly when nearly rigid components retain many vertex {degrees of freedom (DoFs)}. We present DiffGIPC, a GPU-accelerated differentiable IPC framework for optimizing interacting deformable and nearly rigid bodies. Our affine-body derivatives enable {rest-shape} design, initial-state optimization, and articulated control while retaining only twelve motion DoFs per affine body. These gradients account for how changes in geometry, placement, and actuation affect other objects through collision and friction. We preserve these interactions in one coupled FEM-affine adjoint system per frame, reducing the backward-system size without discarding the dependencies that drive the inverse problem. GPU-optimized backward computation further reduces the cost of repeated gradient evaluations. A reusable PyTorch interface connects supported physical parameters to user-defined parameter maps and losses across design, identification, motion, placement, and articulated control. Experiments demonstrate peak speedups over DiffIPC of approximately 150.0x with full-space FEM and 4133.3x with affine bodies for combined forward simulation and backward differentiation.
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All authors Kemeng Huang, Bowen Ren, Xinyu Lu, Minchen Li, Taku komura
Systems and Control
A Performative Control Approach to Day-to-Day Traffic Signal Retiming
Shuchang Jiang, Xinwu Qian, Santiago Segarra et al. (+1 more)
Conventional fixed-time traffic signal optimization and recent day-to-day signal retiming generally model route adaptation under prescribed route-choice behavior and origin-destination (OD) demand, while neglecting the fact that travelers' behavior can be performative to the signal control policy. A signal timing policy learned from historical traffic observations must therefore account for how its own deployment changes the distribution on which it will be evaluated in the future. We formulate this problem as performative signal control under observed traveler responses and characterize performative stability of the coupled policy-demand system. We propose incremental day-to-day retiming using the local policy gradient at the current operating point, estimated offline through simulation-based zeroth-order optimization without an explicit day-to-day demand-response model. This gradual approach provides a feasible path for policy and demand to co-evolve toward stability. Experiments on a downtown Denver network show that our approach improves congestion, throughput, and stability, generalizes across different demand levels, and remains effective when the offline simulator contains mismatched travel demand and routing behaviors.
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All authors Shuchang Jiang, Xinwu Qian, Santiago Segarra, Zhongyuan Zhao
Systems and Control · Cross-listed from cs.MA
Optimizing Team Behavior via Extremum-seeking Control in Multi-agent Reinforcement Learning
Svar Rajankumar Patel and Kshitij Jerath
In cooperative Multi-
Agent Reinforcement Learning (MARL),
agents must learn to coordinate their actions to achieve shared objectives. A key indicator of effective coordination is behavioral diversity, i.e., the
agents' potential to enact distinct yet complementary control policies given the same set of observations. Although behavioral diversity can enable role specialization and richer joint behaviors, its appropriate level is highly context- and task-dependent and often costly to tune. We propose Adaptive Diversity Control (ADiCo), a two-timescale framework that adapts behavioral diversity online during training. On a fast timescale, Diversity Control (DiCo) regulates system diversity toward a target value. On a slower timescale, an Extremum Seeking Controller (ESC) adjusts this target using only mean episodic returns, without requiring an explicit model or analytical gradient of the diversity-performance landscape. We evaluate ADiCo on six configurations across two cooperative MARL tasks, where it maintains task-appropriate diversity levels: low diversity when homogeneous policies are beneficial and higher diversity when heterogeneous policies are required. Through this adaptation, ADiCo matches or exceeds the mean performance of DiCo (best grid-searched diversity target) and Unconstrained DiCo (diversity allowed to emerge without target regulation). These results demonstrate that incorporating classical feedback control into MARL training enables effective coordination by providing a systematic alternative to task-specific manual selection of the diversity target.
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Systems and Control · Cross-listed from math.OC
Compositional Sensitivity Analysis for Multiobjective System Co-Design
Alan Bonomi, Antonio Terpin, Yubo Cai et al. (+3 more)
Sensitivity analysis of coupled systems typically propagates derivatives of single-valued maps, whereas multiobjective optimization generally treats sensitivity at the system level rather than through interconnected subsystems. We develop a mathematical framework for compositional sensitivity analysis in multiobjective system design, building on monotone co-design. Subsystems are represented as design problems (DPs), interconnected through series, parallel, union, intersection, and feedback composition. We introduce tangential DPs (TDPs) to describe first-order variations around an operating point, show that they preserve the structure of DPs, and characterize their queries through graphical derivatives. To account for multiple internal realizations of a system-level operating point, tangential co-design problems compose families of local TDPs by taking a union over these realizations while retaining the original interconnection structure. We show that these compositions yield families of TDPs and establish conditions under which sensitivity analysis commutes with individual composition operations. Beyond infinitesimal sensitivity, we show that bounded tangential models of convex DPs provide tighter outer approximations of feasible finite changes, which can be propagated through co-design problems without union compositions. We conclude with several convex and nonconvex numerical examples that showcase the framework's accuracy and computational benefits.
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All authors Alan Bonomi, Antonio Terpin, Yubo Cai, Yujun Huang, Meshal Alharbi, Gioele Zardini
Systems and Control · Cross-listed from cs.IT
Dynamic Attack Surfaces: A Queue of Branching Processes
Ismail Cosandal, Abdullah Y. Etcibasi, C. Emre Koksal et al. (+1 more)
Modern attack surfaces are inherently dynamic. Vulnerabilities emerge over time and they remain exposed to exploit until defenders discover and patch them. Artificial intelligence (AI) amplifies these dynamics by accelerating both offensive and defensive processes, making modern cybersecurity increasingly a competition among interacting time scales and resources. We capture this competition using a queue of branching processes that couples vulnerability discovery and resource-constrained patching with the evolving attacker population targeting each vulnerability. Our primary risk metric is the cumulative number of exploits a vulnerability experiences before it is patched. We derive the expected cumulative number of exploits under birth-death-immigration (BDI) and rise-decay (RD) models of attacker activity. Under a BDI model of attacker activity, we identify a phase transition: the expected cumulative number of exploits is finite only when attacker growth remains below both the vulnerability discovery rate and the effective patching rate. Across both attacker models, increasing the exploit rate does not necessarily increase the expected number of exploits because faster attacker departure can sufficiently limit the active attacker population. Finally, we show that equal proportional AI amplification of offensive and defensive capabilities need not preserve security: accelerating both sides can shift the defensive bottleneck and move the system into an unbounded-risk regime.
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All authors Ismail Cosandal, Abdullah Y. Etcibasi, C. Emre Koksal, Sennur Ulukus
Systems and Control
Rethinking electrified transportation: The case for dynamic wireless charging
Dipanjan Ghose, Junjie Qin and S. Sivaranjani
Long-term infrastructure investment often requires choosing between an established technology and a capital-intensive alternative that gains value as demand matures. For electric vehicle (EV) charging, this trade-off is between incumbent stationary charging technology and emerging dynamic wireless charging (DWC). DWC could fundamentally alter transportation electrification by enabling high-throughput charging in motion and reducing reliance on large onboard batteries; however, its high upfront cost makes the timing and location of deployment critical. Overcommitting to stationary charging stations (SCS) risks technological lock-in and forgoing the benefits of DWC, whereas premature DWC deployment can strand capital in underutilized corridors. Through a joint investment assessment framework coupling DWC, SCS, and supporting grid infrastructure across nearly 49,000 miles of the US Interstate freight network, we find that higher utilization and longer project lifetimes rapidly shift investment toward DWC, whose share rises sharply from 48% at a project lifetime of 30 years to 100% at 32 years. Contiguous DWC segments can share grid connections, enabling them to provide over six times the rated charging capacity of SCS while requiring 46% less investment in new distribution-grid infrastructure. We also find that a combined DWC and SCS portfolio reaches a maximum lifetime value of USD 60 billion, 63% above the case with only SCS. We further identify specific traffic demand, construction, and grid access conditions that shape where each technology becomes advantageous, yielding actionable thresholds for SCS and DWC deployment that directly inform long-term EV charging infrastructure investment and policy.
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Systems and Control · Cross-listed from cs.LG
Model-Based Online Decision Making via Generative Trajectory Planning
Haldun Balim, Yilun Du and Na Li
Online decision-making faces a tension between efficient inference and long-horizon
reasoning. Value-based methods rely on short-horizon bootstrapping that limits the propagation of long-term information, while trajectory optimization methods plan explicitly but must solve a high-dimensional optimization problem at every decision step. We propose Generative Trajectory Planning (GTP), a model-based framework that addresses this tension by learning a generative model as a proposal distribution over action sequences. Instead of optimizing sequences from scratch, GTP samples candidate sequences from a
diffusion prior and refines them using learned dynamics, reward, and value models, enabling efficient trajectory-level
reasoning. Across continuous control and long-horizon manipulation tasks, GTP matches or exceeds state-of-the-art model-free,
diffusion-policy, and model-based baselines, and remains stable in regimes where strong baselines degrade. Our results suggest that generative models are best understood not as policies, but as structured proposal distributions for planning, offering a new perspective on how to integrate generative modeling with model-based decision-making.
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Systems and Control · Cross-listed from math.OC
Optimal Decay Matrix Control Barrier Functions: Soundness and Completeness for Matrix Safety Constraints
Samuel G. Gessow, Albert Wu, Aaron D. Ames et al. (+1 more)
Many safety requirements, such as keeping a manipulator away from kinematic singularities, a network connected, or a system observable, amount to keeping a state-dependent
matrix positive definite.
Matrix control barrier functions (MCBFs) enforce such requirements with a semidefinite program that minimally modifies a nominal control input. The MCBF constraint bounds how fast the safety margin, the smallest
eigenvalue of the
matrix, may decay. Yet a fixed decay rule is conservative, rejecting safe controls that drive the state quickly toward, but never across, the boundary of the safe set. Scalar optimal decay (SOD) addresses this issue by scaling the decay term by a scalar weight that is optimized together with the control. For scalar barrier functions, SOD then rejects a control at the boundary only if the control would decrease the margin. For MCBFs, however, SOD can reject controls that leave the margin unchanged, and near the safety boundary SOD accepts such controls only at a high cost. We introduce
matrix optimal decay (MOD), which replaces the scalar weight with a
matrix multiplier while keeping the semidefinite program convex. MOD is provably sound and complete, certifying at the boundary exactly the controls that do not decrease the margin. If some control can strictly increase the margin at every boundary point, the MOD-MCBF filtered controller is continuous and renders the safe set forward invariant. We also add an optional recovery condition that drives the system toward the safe set after a violation without affecting soundness or completeness. On a planar two-link arm avoiding singularities, MOD-MCBF certifies safe motions that both a fixed decay rule and SOD reject, and tracks an end-effector target more closely than SOD.
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All authors Samuel G. Gessow, Albert Wu, Aaron D. Ames, Brett T. Lopez
Systems and Control · Cross-listed from math.OC
Dynamic Programming for A Linear-Gaussian Dual Control Problem
Ying Wang, Yuchao Li and Håkan Hjalmarsson
We consider an infinite-horizon discounted dynamic programming (DP) problem for a countable-innovation belief-state model. The model is a surrogate for a linear-Gaussian dual control problem involving an unknown parameter vector and Gaussian observation noise. The surrogate preserves the first two moments of the Gaussian innovation and the resulting posterior second-moment structure. We allow unbounded belief and control spaces and nonnegative stage costs with quadratic growth in the posterior mean and covariance. Using a posterior-moment weight, we show that the Bellman operator is a contraction in a weighted sup-norm space. We characterize the optimal cost as the unique solution of the Bellman equation in this space, establish the existence of optimal stationary policies. We further prove weighted sup-norm convergence of value iteration, policy iteration, and optimistic policy iteration under vanishing approximation errors. The convergence results under vanishing approximation errors rely on general weighted sup-norm contraction properties and are applicable beyond the particular dual control problem considered here.
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Systems and Control · Cross-listed from cs.AI
AgentStripes: An Evidence-Graded Maturity Model for AI Agents
Nutan Kumar Naik, Vijay Prasad Poudel, Aditya Kumar Saroj et al. (+2 more)
Benchmarks report what an Artificial Intelligence (AI)
agent can do on a task suite, but not whether one released version has been engineered with the controls that make it reliable, safe and observable, or how strongly that is shown. This paper proposes AgentStripes, an evidence-graded maturity model for AI
agents. It defines levels (0 to 7 stripes), process areas (12 areas holding 40 capabilities), three gates (basic, safety, proof) that hold an
agent at a level however high its score, and evidence grades E0 to E5. Each applicable capability receives a rating from 0 to 3 and a separate evidence level; these are combined into a credit, aggregated into a severity-weighted score and converted into stripes that the gates limit. Instruments gather evidence,
language-model judges interpret it under explicit criteria, and the arithmetic is deterministic. The model is compared with standards at topic level without a claim of conformance, and an unexecuted validation protocol is specified. An empirical evaluation (exploratory, non-confirmatory) of 30 public Python
agent repositories, with static evidence only, is a demonstration and not a validation: static evidence caps every final stripe at 2, 26 repositories end at one stripe and 4 at two, and this distinction is unreliable, since re-judging changed the final stripe of 4 of 22 repositories (engine, dossier and panel changed), whereas none of 8 panel-only repeats, all at stripe 1, changed. The parameters are expert conventions without outcome based calibration, and the stripes have not been shown to predict reliability or safety.
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All authors Nutan Kumar Naik, Vijay Prasad Poudel, Aditya Kumar Saroj, Saurav Samantray, Abhishek Patel
Systems and Control
Shadow Pricing in Transient Stability-Constrained Optimal Power Flow
Alex Coelho, Araceli Hernandez and Luis Badesa
Transient stability constraints alter the economic dispatch of power systems, while the resulting price signals and their implications for
market settlement remain insufficiently characterized. Existing approaches identify stability-related components in locational marginal prices, without deriving shadow prices for individual assets, such as generators and loads, or analyzing the resulting settlement. To address this gap, this paper derives shadow prices from a transient stability-constrained optimal power flow in which the differential-algebraic equations governing the system dynamics are embedded through direct discretization. Each shadow price decomposes into a nodal energy component and a stability component, the latter formed by the dual variables of the dynamic equations in which the power of the asset appears. Shadow prices are therefore asset-specific: co-located generators and loads are assigned the same nodal energy component, but can be subject to different stability components when their transient behaviors differ. Case studies on the WECC 9-bus system and a modified IEEE 39-bus system show that settlement at the proposed shadow prices allocates the cost of the stability constraints among the assets without any side payment. An asset that reduces the stability margin is charged its stability component, whereas an asset that improves it is paid, and no generator is dispatched at a loss.
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Systems and Control
Evidence-Governed Deployment Readiness for Networked Agents on Industrial OT Infrastructure
Dibyajyoti Nath, Nitya Sathyavageeswaran, Ziran Min et al. (+1 more)
Deploying a workload on a cyber-secure industrial operational-technology (OT) network requires many cross-layer prerequisites to hold at once, including persistent addressing, name resolution, time synchronization, certificate trust, proxy traversal, and reachable industrial-protocol endpoints. These are owned by different teams, yet current tools treat pre-deployment checking, fault attribution, remediation, and ongoing compliance as separate problems, so evidence is rarely reused across stages. As edge platforms come to host self-adapting
agents on shared infrastructure, this fragmentation becomes a governance problem. All four stages ask one question: do a workload's requirements hold on a given network? We present an evidence-governed framework that answers it through a single reference, a versioned network-intent artifact specifying a workload's cross-layer requirements for a specific release. A coverage-driven test loop attaches tested evidence to each requirement before deployment. At a site, readiness is reported as ready, not ready, or undecidable, separating untested requirements from genuine failures; a repair step proposes fixes that preserve co-located workloads; and a drift-watch component re-evaluates the site on change, extending assurance across the workload's lifetime. A multi-workload OPC UA commissioning case illustrates all four stages. By detecting runtime deviations and suspending an
agent's guarantees until a refreshed artifact is registered, the framework grounds auditable governance for adapting
agent collectives on shared industrial infrastructure.
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All authors Dibyajyoti Nath, Nitya Sathyavageeswaran, Ziran Min, Pavan Athreya Narasimha Murthy
Systems and Control
Impact of Security Modeling Fidelity on Hyperscale Data Center Hosting Capacity Assessment
Haoxiang Wan, Linhan Fang, Jonghwan Kwon et al. (+5 more)
This paper assesses hyperscale data center hosting capacity in transmission grids. Hosting capacity (HC) analysis quantifies the maximum additional load a bus can accommodate without violating operational constraints. This paper defines data center HC as the maximum time-invariant active power demand at a single target bus over a 24-hour peak-day horizon, and evaluates it under four models of increasing fidelity (i.e., the extent to which the model enforces real-world operational security requirements) on the modified IEEE 24-bus system: (i) DC optimal power flow (DC-OPF) without unit commitment (UC), (ii) basic SCUC (security-constrained unit commitment) without reliability constraints, (iii) SCUC with spinning reserve, and (iv) N-SCUC with both reserve and branch-contingency security. Comparing these models quantifies the impact of unit commitment, reserve requirements, and N-1 security on bus-level HC estimates. The N-SCUC yields an average HC of 444 MW; omitting N-1 security raises this to 532 MW, a 19.8% overestimation. The DC-OPF and basic SCUC models further overestimate HC, indicating a trade-off between model complexity and estimation accuracy.
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All authors Haoxiang Wan, Linhan Fang, Jonghwan Kwon, Jesus Silva-Rodriguez, Jianyi Yang, Jaya Yellajosula, Fan Jiang, Xingpeng Li
Systems and Control
Impact of Data Center Load on Co-Optimized Generation and Transmission Expansion Planning with Embedded Unit Commitment
Haoxiang Wan, Xingpeng Li and Jonghwan Kwon
The rapid growth of hyperscale data centers challenges grid expansion planning, yet existing studies focus largely on operational flexibility rather than long-term investment decisions. This paper examines how large data center loads sited at multiple buses affect co-optimized Transmission Expansion Planning (TEP) and Generation Expansion Planning (GEP). The problem is formulated as a multi-year, Unit Commitment (UC)-embedded mixed-integer linear program on the modified IEEE 24-bus Reliability Test System (RTS-24) with Electric Reliability Council of Texas (ERCOT)-calibrated renewable energy profiles. A 500 MW data center load, about 17.5% of the base-year peak, is allocated across four buses by voltage level, generation headroom, renewable co-location, and network connectivity. Integrating this load raises the 15-year net present value of total system cost by 1,794 million USD (+61.6%) and the system-average locational marginal price (LMP) by 45.2%, with operating cost dominating capital investment (84.2% versus 15.8%). Renewable curtailment remains negligible: existing renewables are already near full utilization, so the added demand is served almost entirely by incremental thermal generation. Most notably, the load shifts the optimal plan from transmission-only to generation-only, replacing the base-case 230 kV reinforcement with a new 400 MW combined-cycle gas turbine and showing that strategically sited generation can substitute for network reinforcement.
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Systems and Control
Computation of Price Signals for Controlling Flexible Prosumers in Distribution Grids Incorporating Stochasticity and Fairness
Plouton Grammatikos, Chloé Voirol, Pierre Roduit et al. (+1 more)
This paper proposes a method for distribution system operators (DSOs) to design spatially fair dynamic price signals for flexible prosumers, accounting for both distribution grid constraints and the
stochastic behavior of prosumers. Since locational pricing can introduce price unfairness when grid constraints are active, the proposed approach includes mechanisms to enforce price
fairness across the network. The method involves the DSO solving a scenario-based optimal power flow (OPF) problem to derive, similarly to locational marginal prices in transmission systems, the Lagrange multipliers associated with grid constraints and to compute individualized price signals for each prosumer under various PV generation and demand scenarios. Subsequently, two methods are introduced to aggregate these individualized prices into a single fair price across prosumers. Their performance is evaluated through simulations on a model distribution grid, comparing prosumer revenues, grid constraint violations, and analyzing the impact of
fairness on reducing cost variance among prosumers.
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All authors Plouton Grammatikos, Chloé Voirol, Pierre Roduit, Fabrizio Sossan
Systems and Control
Adaptive Handling of Deadline Misses via Stochastic MPC
Melanie Gallant, Paolo Pazzaglia, Christoph Mark et al. (+2 more)
Real-time control systems may experience sporadic computational overloads that cause controller executions to overrun their expected deadlines, affecting the timely update of control inputs. Such overruns are classically handled with static decisions, e.g., by either stopping and discarding the late execution at the deadline instant, or continuing it while skipping the next activations. This paper first demonstrates that such static policies can lead to suboptimal performance when the controlled system undergoes varying operating conditions, and thus proposes a control-driven hybrid overrun strategy that optimally adapts the deadline miss handling at runtime. We model the closed loop as a Markov jump linear system, whose modes encode overrun histories, and we formulate a scenario-tree-based
stochastic model predictive control problem that predicts and optimizes over possible sequences of deadline hits and misses. We evaluate our proposal on linear
benchmarks including a double integrator and an aircraft pitch system.
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All authors Melanie Gallant, Paolo Pazzaglia, Christoph Mark, Kevin Schmidt, Martina Maggio
Systems and Control
Infrastructure-Aware Runtime Protection of Co-Deployed AI Services at the Industrial Edge
Nitya Sathyavageeswaran, Dibyajyoti Nath, Ziran Min et al. (+1 more)
In modern production lines, a single edge device simultaneously runs multiple AI services like a torque-control model, a
vision inspector, a predictive maintenance routine. All these services run on the same CPU and same shared network segment. When the shared network segment saturates, the services do not crash. The services just produce outputs that are inaccurate or stale, and no longer reflect the plant's conditions. Our paper targets this class of silent, partial failure scenarios in Industrial Edge Systems. We treat each co-deployed service as an autonomous
agent. We introduce the Runtime Criticality Score (RCS), which each service
agent uses to express at any moment, its intrinsic utility and survival urgency under resource constraints. The score is computed from six normalized dimensions covering physical consequence, inference quality, input freshness, recent availability stress, actuator coupling, and available protective actions on the host. Its dimension weights are adjusted online through a lightweight
Bayesian update driven by the type of disturbance currently observed, so the same service is ranked differently under a network delay than under thermal throttling.
Agents publish their RCS to peers over an open, peer-to-peer discovery channel, and use it to negotiate three things without a central controller: (i) local threshold enforcement on their own host, (ii) migration to a willing peer when the current host can no longer protect them, (iii) reassignment of the coordination role when the incumbent coordinator is itself impaired. Beyond the immediate benefit of catching silent degradation, the framework contributes a concrete instance of decentralized, physically grounded
agent coordination.
Agents belonging to different owners can advertise capabilities, bid for protection, and reorganize across an open network.
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All authors Nitya Sathyavageeswaran, Dibyajyoti Nath, Ziran Min, Pavan Athreya Narasimha Murthy
Systems and Control · Cross-listed from cs.CR
BALLAST: Physics-Aware Moving Target Defense for Industrial Control Systems Using a Digital Twin Safety Oracle
Oscar Mogollon-Gutierrez, Mohammadhossein Homaei, Alberto Lopez-Trigo et al. (+2 more)
Industrial Control Systems run on static network configurations, which leaves an attacker unlimited time to map the plant. Moving Target Defense answers this by shuffling addresses, but almost every published scheme never checks whether the reconfiguration will itself disturb the physical process it protects, so the defense can destabilise a control loop or trip an actuator. We present BALLAST, which places a learned Digital Twin between the security policy and the plant as a safety oracle. A physics-informed LSTM surrogate forecasts the physical effect of a candidate network change, a reinforcement learning
agent trades security
entropy against process stability, and a connection-draining handover lets in-flight Modbus transactions complete before the old mapping is withdrawn. An explicit shield rejects any action whose predicted trajectory would leave the safe operating envelope. On the SWaT water treatment testbed, Ballast cut the success rate of an adversary's reconnaissance sweep by 85 percent and recorded no safety violation across 50 runs, a 95 percent upper bound of 5.8 percent on the violation rate, against five violations and a tracking error 19 times larger for RL-MTD, the closest published analogue to our design. Measured against the strongest baseline on the physical metrics, a threshold-triggered scheme that itself records one violation rather than RL-MTD's five, Ballast still improves the tracking error by a factor of 2.5 and removes that remaining violation. The gain costs a small and deliberate concession in raw deterrence. Twin inference meets the 50 ms budget on a workstation core and an embedded GPU, and is projected to meet it on both edge CPUs when the three envelope stages are evaluated in a single batched call; the complete decision-to-enforcement path was measured only on the workstation.
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All authors Oscar Mogollon-Gutierrez, Mohammadhossein Homaei, Alberto Lopez-Trigo, Andres Caro, Mar Avila
Systems and Control
On Over-Conservatism Under Low-Probability Extreme Events: A KL-Regularized Wasserstein Distributionally Robust Planning Method
Xinda Shen, Yifan Su, Bo Hu et al. (+3 more)
The increasing penetration of renewable energy makes power systems more vulnerable to low-probability extreme events, elevating system planning to a critical role in ensuring reliability. Distributionally robust optimization (DRO) based on the Wasserstein distance is widely used to address this challenge. However, we find that the conventional Wasserstein ambiguity set tends to excessively amplify the probabilities of low-probability extreme events, leading to over-conservative investment decisions. To mitigate this over-conservatism, this paper proposes a novel ambiguity set that modifies the Wasserstein distance with a Kullback-Leibler (KL) divergence regularization term. By leveraging the KL divergence's sensitivity to relative probability changes, the proposed set effectively curbs the unrealistic magnification of low-probability events. A two-stage distributionally robust planning model built upon this new ambiguity set is developed to optimize the installed capacity of photovoltaic, wind, and energy storage. The complex model is then reformulated as an equivalent exponential cone program with strict theoretical proofs, making it computationally tractable. Numerical results on the PJM 5-bus and IEEE 118-bus systems reveal the over-conservatism of standard Wasserstein DRO under low-probability extreme events and validate the effectiveness and efficiency of the proposed planning model and solution
algorithm.
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All authors Xinda Shen, Yifan Su, Bo Hu, Yanbo Chen, Changzheng Shao, Tao Wu
Systems and Control
Fast Analytical Method for Resource Adequacy Assessment in Renewable-Rich Power Systems
Seyed Amir Hosseini, Mohammad Reza Karimi Gharigh, Antonello Monti et al. (+2 more)
This paper proposes a novel analytical method for resource adequacy assessment in renewable rich power systems. The approach begins by applying the stress strength method to estimate two key resource adequacy indices: loss of load expectation (LOLE) and expected energy not supplied (EENS). This method requires defining the probability distribution functions (PDFs) for both the generation side and the load side. The load is assumed to follow a normal probability distribution, while the generation side probability distribution is derived from the combined PDFs of conventional, wind, and solar power sources. The approach models the cumulative impact of uncertainties on resource adequacy using the central limit
theorem (CLT), and improves accuracy with a cumulant based Gram Charlier expansion when the CLT alone is insufficient. The effectiveness of the proposed method is validated through Monte Carlo simulations conducted on the IEEE single area and multi-area RTS-96 test systems. The results demonstrate that the proposed method achieves a substantial reduction in computational time, which is crucial in the context of operational planning, without sacrificing accuracy. Furthermore, its performance improves as renewable penetration increases, making it particularly suitable for future power systems operation and planning.
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All authors Seyed Amir Hosseini, Mohammad Reza Karimi Gharigh, Antonello Monti, Peter Palensky, Saeed Peyghami
Systems and Control
Data-Driven Surrogate-Model for Real-Time Loads Prediction in Floating Wind Farms
Federico Bellini, Majid Ebrahimi, Alessandro Fontanella et al. (+4 more)
This work presents a data-driven load-surrogate modelling framework for real-time control of floating offshore wind farms. The proposed methodology uses mid-fidelity simulations performed with OpenFAST and FAST.Farm together with MLife fatigue analysis to generate Damage Equivalent Load (DEL)
datasets of floating wind turbines under wake steering conditions. A two-stage surrogate modelling approach is adopted: first, a Feed-Forward Neural Network (FFNN) is trained using data from simulations of a single floating wind turbine, while a second stage corrects the residual error between the FFNN predictions and FAST.Farm simulations using either Gaussian Process Regression (GPR) or an additional FFNN. The results show that the proposed framework can estimate DELs with errors generally below 15% for tower base bending moment, blade root bending moment, yaw bearing moment, and fairlead tension, while maintaining computational times compatible with real-time applications. The study also highlights the challenges related to generalization of the load-surrogate model under varying environmental and control conditions, especially for large floating wind farms. Overall, the proposed methodology demonstrates the potential of surrogate-based DEL estimation for load-aware floating wind farm control applications.
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All authors Federico Bellini, Majid Ebrahimi, Alessandro Fontanella, Marcus Becker, Jan-Willem Van Wingerden, Sara Muggiasca, Marco Belloli
Systems and Control
Generic Local Identifiability Does Not Imply Generic Global Identifiability in Linear Networks
Anders Hansson
We give a counterexample to the implication from generic local to generic global identifiability for linear networks with a fixed
topology, independently unknown edge coefficients, and selected excited and measured nodes. A six-node network with nine unknown coefficients has exactly two compatible parameter vectors generically. Adding six excited and measured nodes preserves this ambiguity while making the added coefficients directly recoverable. The resulting twelve-node structure has forty-two independent unknown coefficients and zero diagonal. For every positive bound on the largest
eigenvalue magnitude, a nonempty open set of parameters has two distinct compatible networks satisfying that bound. This holds for both real and complex coefficients. Other open regions have exactly one admissible solution. The proof uses a rational transformation exchanging the two solutions and a common limiting
matrix whose fourth power vanishes. Exact rational-number certificates verify an explicit pair. The construction also extends algebraically when the unknown coordinates are whole rational transfer functions. Genericity in prescribed numerator and denominator coefficient families, and the requirement that every transfer function vanish at infinite frequency, remain separate questions.
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Systems and Control
Preliminary Exploration of Large Models Based on Self-Supervised Learning for Health Monitoring of Power Electronic Converters
Xinyuan Liao, Xiangtian Deng, Yang Liu et al. (+3 more)
Current artificial intelligence (AI) methods for power electronics converters make limited use of the abundant unlabeled data generated during real-world operation. This underexplored resource holds significant potential for advancing model performance. To address this gap, this paper presents PE-TSFM, a time-series foundation model tailored for power module health monitoring. The model adopts the PatchTST architecture combined with self-supervised learning and is trained on more than 143 million timestamps, where over 98% of this training data is unlabeled and collected directly from an operating traction converter. In degradation classification tasks, PE-TSFM achieves an accuracy of 87%, considerably surpassing five conventional time-series AI models whose performance ranges from 27% to 45%. These results demonstrate the model's strong few-shot learning capability and generalization across different operating conditions. This work promotes a shift from narrowly focused models in power electronics toward unified, scalable foundation models that can unlock value from large-scale unlabeled operational data.
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All authors Xinyuan Liao, Xiangtian Deng, Yang Liu, Zhaobing Cai, Haoran Wang, Yi Zhang
Systems and Control · Cross-listed from math.OC
Control Realization of Schrödinger Bridges over Discrete-Time Nonlinear Systems via Maximum-Entropy Distribution Steering
Kaito Ito and Kenji Kashima
This paper shows how a Schrodinger bridge (SB) structure emerges from maximum-
entropy (MaxEnt) distribution steering of discrete-time nonlinear systems. We also establish that the resulting bridge can be realized by an optimal control policy. We consider general state and input costs, non-Gaussian endpoint distributions, and input constraints. Assuming the existence of a feasible policy with finite objective value, we show that the state-path measure under a MaxEnt optimal control policy is the unique SB. The reference measure of this SB is induced by an optimal policy of the corresponding terminal-unconstrained problem. Moreover, we give an equivalent characterization of the optimal state-path measure using a killed reference and its normalized Feynman–Kac measure, thereby interpreting the state cost as killing. We then characterize the finite-cost feasibility by the existence of an endpoint coupling having finite Kullback–Leibler divergence from the reference endpoint distribution. For compactly supported marginals with bounded target density, we derive reachability-based sufficient conditions guaranteeing this feasibility. We further provide specializations to fully actuated control-affine and reachable linear systems.
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Systems and Control
Enforcing a Strict Safety Margin in LLM Text Generation via Barrier Lyapunov Control
Anand Singh, Shubham Singh, Anjali Tiwari et al. (+1 more)
Large
language models are increasingly deployed without formal guarantees against toxic text generation. Existing control barrier function (CBF) based safety filters can keep the generation trajectory within a toxicity-free region, but do not enforce a positive distance from its boundary, allowing formally safe trajectories to approach the toxicity threshold arbitrarily closely and leaving little buffer against modelling errors or disturbances. We address this limitation with a Barrier Lyapunov Function (BLF) safety filter that enforces a prescribed clearance margin throughout generation while retaining the continuous-time
transformer dynamics, including feedforward layers. The resulting safe input set is a half-space defined by a constraint affine in the control input, and is non-empty under certified surjectivity and classifier-nondegeneracy conditions. For margin-compatible initial conditions we prove that the clearance margin is never violated and that the prescribed clearance is restored exponentially, yielding an explicit clearance floor, and show that, as the margin vanishes, the admissible trajectories satisfy the exponential second-order CBF condition. We further derive an explicitly computable sufficient disturbance budget under which the clearance guarantee degrades gracefully. Simulations on a small
transformer illustrate the resulting improvement in safety clearance.
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All authors Anand Singh, Shubham Singh, Anjali Tiwari, Katsuki Fujisawa