pwc-archive/papers-with-abstracts huggingface dataset:
The Ground Cost for Optimal Transport of Angular Velocity
Authors: Karthik Elamvazhuthi, Abhishek Halder
Date: 2025-04-04 00:00:00

We revisit the optimal transport problem over angular velocity dynamics given by the controlled Euler equation. The solution of this problem enables stochastic guidance of spin states of a rigid body (e.g., spacecraft) over hard deadline constraint by transferring a given initial state statistics to a desired terminal state statistics. This is an instance of generalized optimal transport over a nonlinear dynamical system. While prior work has reported existence-uniqueness and numerical solution of this dynamical optimal transport problem, here we present structural results about the equivalent Kantorovich a.k.a. optimal coupling formulation. Specifically, we focus on deriving the ground cost for the associated Kantorovich optimal coupling formulation. The ground cost equals to the cost of transporting unit amount of mass from a specific realization of the initial or source joint probability measure to a realization of the terminal or target joint probability measure, and determines the Kantorovich formulation. Finding the ground cost leads to solving a structured deterministic nonlinear optimal control problem, which is shown to be amenable to an analysis technique pioneered by Athans et. al. We show that such techniques have broader applicability in determining the ground cost (thus Kantorovich formulation) for a class of generalized optimal mass transport problems involving nonlinear dynamics with translated norm-invariant drift.
Performance-Aware Control of Modular Batteries For Fast Frequency Response
Authors: Yutong He, Guangchun Ruan, Haiwang Zhong
Date: 2025-04-04 00:00:00
Tasks: Scheduling

Modular batteries can be aggregated to deliver frequency regulation services for power grids. Although utilizing the idle capacity of battery modules is financially attractive, it remains challenging to consider the heterogeneous module-level characteristics such as dynamic operational efficiencies and battery degradation. In addition, real-time decision making within seconds is required to enable fast frequency response. In order to address these issues, this paper proposes a performance-aware scheduling approach for battery modules to deliver fast frequency response (FFR) support. In particular, the conduction loss and switching loss of battery packs as well as converters are captured within a mix-integer quadratic constrained program (MIQCP). The cycle-based aging model identifies the aging cost of battery modules during frequent cycling by introducing the aging subgradient calculation and linearization. Case studies based on real-world battery data show that the proposed scheduling approach can effectively reduce power loss cost by nearly 28%-57% and battery aging cost by 4%-15% compared to conventional methods, which can also enhance the SoC balance.
Oscillatory Associative Memory with Exponential Capacity
Authors: Taosha Guo, Arie Ogranovich, Arvind R. Venkatakrishnan, Madelyn R. Shapiro, Francesco Bullo, Fabio Pasqualetti
Date: 2025-04-04 00:00:00

The slowing of Moore's law and the increasing energy demands of machine learning present critical challenges for both the hardware and machine learning communities, and drive the development of novel computing paradigms. Of particular interest is the challenge of incorporating memory efficiently into the learning process. Inspired by how human brains store and retrieve information, associative memory mechanisms provide a class of computational methods that can store and retrieve patterns in a robust, energy-efficient manner. Existing associative memory architectures, such as the celebrated Hopfield model and oscillatory associative memory networks, store patterns as stable equilibria of network dynamics. However, the capacity (i.e. the number of patterns that a network can memorize normalized by their number of nodes) of existing oscillatory models have been shown to decrease with the size of the network, making them impractical for large-scale, real-world applications. In this paper, we propose a novel associative memory architecture based on Kuramoto oscillators. We show that the capacity of our associative memory network increases exponentially with network size and features no spurious memories. In addition, we present algorithms and numerical experiments to support these theoretical findings, providing guidelines for the hardware implementation of the proposed associative memory networks.
Moving Target Defense Against Adversarial False Data Injection Attacks In Power Grids
Authors: Yexiang Chen, Subhash Lakshminarayana, H. Vincent Poor
Date: 2025-04-03 00:00:00
Tasks: Adversarial Attack

Machine learning (ML)-based detectors have been shown to be effective in detecting stealthy false data injection attacks (FDIAs) that can bypass conventional bad data detectors (BDDs) in power systems. However, ML models are also vulnerable to adversarial attacks. A sophisticated perturbation signal added to the original BDD-bypassing FDIA can conceal the attack from ML-based detectors. In this paper, we develop a moving target defense (MTD) strategy to defend against adversarial FDIAs in power grids. We first develop an MTD-strengthened deep neural network (DNN) model, which deploys a pool of DNN models rather than a single static model that cooperate to detect the adversarial attack jointly. The MTD model pool introduces randomness to the ML model's decision boundary, thereby making the adversarial attacks detectable. Furthermore, to increase the effectiveness of the MTD strategy and reduce the computational costs associated with developing the MTD model pool, we combine this approach with the physics-based MTD, which involves dynamically perturbing the transmission line reactance and retraining the DNN-based detector to adapt to the new system topology. Simulations conducted on IEEE test bus systems demonstrate that the MTD-strengthened DNN achieves up to 94.2% accuracy in detecting adversarial FDIAs. When combined with a physics-based MTD, the detection accuracy surpasses 99%, while significantly reducing the computational costs of updating the DNN models. This approach requires only moderate perturbations to transmission line reactances, resulting in minimal increases in OPF cost.
Online Learning for Nonlinear Dynamical Systems without the I.I.D. Condition
Authors: Lantian Zhang, Silun Zhang
Date: 2025-04-03 00:00:00
Tasks: parameter estimation

This paper investigates online identification and prediction for nonlinear stochastic dynamical systems. In contrast to offline learning methods, we develop online algorithms that learn unknown parameters from a single trajectory. A key challenge in this setting is handling the non-independent data generated by the closed-loop system. Existing theoretical guarantees for such systems are mostly restricted to the assumption that inputs are independently and identically distributed (i.i.d.), or that the closed-loop data satisfy a persistent excitation (PE) condition. However, these assumptions are often violated in applications such as adaptive feedback control. In this paper, we propose an online projected Newton-type algorithm for parameter estimation in nonlinear stochastic dynamical systems, and develop an online predictor for system outputs based on online parameter estimates. By using both the stochastic Lyapunov function and martingale estimation methods, we demonstrate that the average regret converges to zero without requiring traditional persistent excitation (PE) conditions. Furthermore, we establish a novel excitation condition that ensures global convergence of the online parameter estimates. The proposed excitation condition is applicable to a broader class of system trajectories, including those violating the PE condition.
Route Recommendations for Traffic Management Under Learned Partial Driver Compliance
Authors: Heeseung Bang, Jung-Hoon Cho, Cathy Wu, Andreas A. Malikopoulos
Date: 2025-04-03 00:00:00
Tasks: Management, Stochastic Optimization

In this paper, we aim to mitigate congestion in traffic management systems by guiding travelers along system-optimal (SO) routes. However, we recognize that most theoretical approaches assume perfect driver compliance, which often does not reflect reality, as drivers tend to deviate from recommendations to fulfill their personal objectives. Therefore, we propose a route recommendation framework that explicitly learns partial driver compliance and optimizes traffic flow under realistic adherence. We first compute an SO edge flow through flow optimization techniques. Next, we train a compliance model based on historical driver decisions to capture individual responses to our recommendations. Finally, we formulate a stochastic optimization problem that minimizes the gap between the target SO flow and the realized flow under conditions of imperfect adherence. Our simulations conducted on a grid network reveal that our approach significantly reduces travel time compared to baseline strategies, demonstrating the practical advantage of incorporating learned compliance into traffic management.
Dynamical Simulation Model of the Pyro-Process in Cement Clinker Production
Authors: Jan Lorenz Svensen, Wilson Ricardo Leal da Silva, Zhanhao Zhang, Steen Hørsholt, John Bagterp Jørgensen
Date: 2025-04-02 00:00:00

This study presents a dynamic simulation model for the pyro-process of clinker production in cement plants. The study aims to construct a simulation model capable of replicating the real-world dynamics of the pyro-process to facilitate research into the improvements of operation, i.e., the development of alternative strategies for reducing CO2 emissions and ensuring clinker quality, production, and lowering fuel consumption. The presented model is an index-1 differential-algebraic equation (DAE) model based on first engineering principles and modular approaches. Using a systematic approach, the model is described on a detailed level that integrates geometric aspects, thermo-physical properties, transport phenomena, stoichiometry and kinetics, mass and energy balances, and algebraic volume and energy conservations. By manually calibrating the model to a steady-state reference, we provide dynamic simulation results that match the expected reference performance and the expected dynamic behavior from the industrial practices.
AI-Enhanced Resilience in Power Systems: Adversarial Deep Learning for Robust Short-Term Voltage Stability Assessment under Cyber-Attacks
Authors: Yang Li, Shitu Zhang, Yuanzheng Li
Date: 2025-03-31 00:00:00
Tasks: Generative Adversarial Network, Graph Attention

In the era of Industry 4.0, ensuring the resilience of cyber-physical systems against sophisticated cyber threats is increasingly critical. This study proposes a pioneering AI-based control framework that enhances short-term voltage stability assessments (STVSA) in power systems under complex composite cyber-attacks. First, by incorporating white-box and black-box adversarial attacks with Denial-of-Service (DoS) perturbations during training, composite adversarial attacks are implemented. Second, the application of Spectral Normalized Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (SNCWGAN-GP) and Fast Gradient Sign Method (FGSM) strengthens the model's resistance to adversarial disturbances, improving data quality and training stability. Third, an assessment model based on Long Short-Term Memory (LSTM)-enhanced Graph Attention Network (L-GAT) is developed to capture dynamic relationships between the post-fault dynamic trajectories and electrical grid topology. Experimental results on the IEEE 39-bus test system demonstrate the efficacy and superiority of the proposed method in composite cyber-attack scenarios. This contribution is pivotal to advancing AI-based resilient control strategies for nonlinear dynamical systems, marking a substantial enhancement in the security of cyber-physical systems.
Event-Based Distributed Linear Quadratic Gaussian for Multi-Robot Coordination with Localization Uncertainty
Authors: Tohid Kargar Tasooji, Sakineh Khodadadi
Date: 2025-03-28 00:00:00

This paper addresses the problem of event-based distributed Linear Quadratic Gaussian (LQG) control for multirobot coordination under localization uncertainty. An event-triggered LQG rendezvous control strategy is proposed to ensure coordinated motion while reducing communication overhead. The design framework decouples the LQG controller from the event-triggering mechanism, although the scheduler parameters critically influence rendezvous performance. We establish stochastic stability for the closed-loop multi-robot system and demonstrate that a carefully tuned event-triggering scheduler can effectively balance rendezvous accuracy with communication efficiency by limiting the upper bound of the rendezvous error while minimizing the average transmission rate. Experimental results using a group of Robotarium mobile robots validate the proposed approach, confirming its efficacy in achieving robust coordination under uncertainty.
Exploration of Multi-Element Collaborative Research and Application for Modern Power System Based on Generative Large Models
Authors: Lu Cheng, Qixiu Zhang, Beibei Xu, Zhiwei Huang, Cirun Zhang, Yanan Lyu, Fan Zhang
Date: 2025-03-26 00:00:00
Tasks: Management, Scheduling

The transition to intelligent, low-carbon power systems necessitates advanced optimization strategies for managing renewable energy integration, energy storage, and carbon emissions. Generative Large Models (GLMs) provide a data-driven approach to enhancing forecasting, scheduling, and market operations by processing multi-source data and capturing complex system dynamics. This paper explores the role of GLMs in optimizing load-side management, energy storage utilization, and electricity carbon, with a focus on Smart Wide-area Hybrid Energy Systems with Storage and Carbon (SGLSC). By leveraging spatiotemporal modeling and reinforcement learning, GLMs enable dynamic energy scheduling, improve grid stability, enhance carbon trading strategies, and strengthen resilience against extreme weather events. The proposed framework highlights the transformative potential of GLMs in achieving efficient, adaptive, and low-carbon power system operations.
Event-Triggered Nonlinear Model Predictive Control for Cooperative Cable-Suspended Payload Transportation with Multi-Quadrotors
Authors: Tohid Kargar Tasooji, Sakineh Khodadadi, Guangjun Liu
Date: 2025-03-26 00:00:00
Tasks: Model Predictive Control

Autonomous Micro Aerial Vehicles (MAVs), particularly quadrotors, have shown significant potential in assisting humans with tasks such as construction and package delivery. These applications benefit greatly from the use of cables for manipulation mechanisms due to their lightweight, low-cost, and simple design. However, designing effective control and planning strategies for cable-suspended systems presents several challenges, including indirect load actuation, nonlinear configuration space, and highly coupled system dynamics. In this paper, we introduce a novel event-triggered distributed Nonlinear Model Predictive Control (NMPC) method specifically designed for cooperative transportation involving multiple quadrotors manipulating a cable-suspended payload. This approach addresses key challenges such as payload manipulation, inter-robot separation, obstacle avoidance, and trajectory tracking, all while optimizing the use of computational and communication resources. By integrating an event-triggered mechanism, our NMPC method reduces unnecessary computations and communication, enhancing energy efficiency and extending the operational range of MAVs. The proposed method employs a lightweight state vector parametrization that focuses on payload states in all six degrees of freedom, enabling efficient planning of trajectories on the SE(3) manifold. This not only reduces planning complexity but also ensures real-time computational feasibility. Our approach is validated through extensive simulation, demonstrating its efficacy in dynamic and resource-constrained environments.
Curvature-Constrained Vector Field for Motion Planning of Nonholonomic Robots
Authors: Yike Qiao, Xiaodong He, An Zhuo, Zhiyong Sun, Weimin Bao, Zhongkui Li
Date: 2025-03-25 00:00:00
Tasks: Motion Planning

Vector fields are advantageous in handling nonholonomic motion planning as they provide reference orientation for robots. However, additionally incorporating curvature constraints becomes challenging, due to the interconnection between the design of the curvature-bounded vector field and the tracking controller under underactuation. In this paper, we present a novel framework to co-develop the vector field and the control laws, guiding the nonholonomic robot to the target configuration with curvature-bounded trajectory. First, we formulate the problem by introducing the target positive limit set, which allows the robot to converge to or pass through the target configuration, depending on different dynamics and tasks. Next, we construct a curvature-constrained vector field (CVF) via blending and distributing basic flow fields in workspace and propose the saturated control laws with a dynamic gain, under which the tracking error's magnitude decreases even when saturation occurs. Under the control laws, kinematically constrained nonholonomic robots are guaranteed to track the reference CVF and converge to the target positive limit set with bounded trajectory curvature. Numerical simulations show that the proposed CVF method outperforms other vector-field-based algorithms. Experiments on Ackermann UGVs and semi-physical fixed-wing UAVs demonstrate that the method can be effectively implemented in real-world scenarios.
A Class of Hierarchical Sliding Mode Control based on Extended Kalman filter for Quadrotor UAVs
Authors: Van Chung Nguyen, Hung Manh La
Date: 2025-03-24 00:00:00

This study introduces a novel methodology for controlling Quadrotor Unmanned Aerial Vehicles, focusing on Hierarchical Sliding Mode Control strategies and an Extended Kalman Filter. Initially, an EKF is proposed to enhance robustness in estimating UAV states, thereby reducing the impact of measured noises and external disturbances. By locally linearizing UAV systems, the EKF can mitigate the disadvantages of the Kalman filter and reduce the computational cost of other nonlinear observers. Subsequently, in comparison to other related work in terms of stability and computational cost, the HSMC framework shows its outperformance in allowing the quadrotor UAVs to track the references. Three types of HSMC Aggregated HSMC, Incremental HSMC, and Combining HSMC are investigated for their effectiveness in tracking reference trajectories. Moreover, the stability of the quadrotor UAVs is rigorously analyzed using the Lyapunov stability principle. Finally, experimental results and comparative analyses demonstrate the efficacy and feasibility of the proposed methodologies.
The AI Cosmologist I: An Agentic System for Automated Data Analysis
Authors: Adam Moss
Date: 2025-04-04 00:00:00
Tasks: scientific discovery

We present the AI Cosmologist, an agentic system designed to automate cosmological/astronomical data analysis and machine learning research workflows. This implements a complete pipeline from idea generation to experimental evaluation and research dissemination, mimicking the scientific process typically performed by human researchers. The system employs specialized agents for planning, coding, execution, analysis, and synthesis that work together to develop novel approaches. Unlike traditional auto machine-learning systems, the AI Cosmologist generates diverse implementation strategies, writes complete code, handles execution errors, analyzes results, and synthesizes new approaches based on experimental outcomes. We demonstrate the AI Cosmologist capabilities across several machine learning tasks, showing how it can successfully explore solution spaces, iterate based on experimental results, and combine successful elements from different approaches. Our results indicate that agentic systems can automate portions of the research process, potentially accelerating scientific discovery. The code and experimental data used in this paper are available on GitHub at https://github.com/adammoss/aicosmologist. Example papers included in the appendix demonstrate the system's capability to autonomously produce complete scientific publications, starting from only the dataset and task description
Leapfrogging of a deterministic model for microeconomic systems in competitive markets
Authors: Alain M. Dikande, H. Ntahombagana Matabaro
Date: 2025-03-17 00:00:00
Tasks: Time Series Analysis

The Behrens-Feichtinger model provides a deterministic picture for the co-evolution of sales of two firms, producing the same goods and competing in a common market. The model involves an active investment strategy such that the temporary investment of each of the two firms depends on its relative position in the market. In this work we are interested in a specific regime of evolution referred to as leapfrogging regime, in which each firm has the possibility to dominate the market alternately during some finite period of time. We examine conditions favoring the leapfrogging dynamics of the model by introducing two appropriate variables, namely the sale difference and total sale of the two firms at any time. An analysis of stability of fixed points of the resulting coupled discrete nonlinear equations is carried out, and the bifurcation diagrams of the sale difference and sum with respect to the elasticity coefficient, are generated. A time-series analysis suggests that the leapfrogging regime, characterized by periodic oscillations of the sale difference from positive to negative branches, is stabilized by specific values of characteristic parameters of the model and when in addition the elasticity coefficient, related to the difference in investment strategies of the two firms, is reasonably high. A positive sign of cumulated sales at any time is required to ensure the availability of goods during the leapfrogging dynamics.
Generating ensembles of spatially-coherent in-situ forecasts using flow matching
Authors: David Landry, Claire Monteleoni, Anastase Charantonis
Date: 2025-04-04 00:00:00

We propose a machine-learning-based methodology for in-situ weather forecast postprocessing that is both spatially coherent and multivariate. Compared to previous work, our Flow MAtching Postprocessing (FMAP) better represents the correlation structures of the observations distribution, while also improving marginal performance at the stations. FMAP generates forecasts that are not bound to what is already modeled by the underlying gridded prediction and can infer new correlation structures from data. The resulting model can generate an arbitrary number of forecasts from a limited number of numerical simulations, allowing for low-cost forecasting systems. A single training is sufficient to perform postprocessing at multiple lead times, in contrast with other methods which use multiple trained networks at generation time. This work details our methodology, including a spatial attention transformer backbone trained within a flow matching generative modeling framework. FMAP shows promising performance in experiments on the EUPPBench dataset, forecasting surface temperature and wind gust values at station locations in western Europe up to five-day lead times.