pwc-archive/papers-with-abstracts huggingface dataset:
Automated Heuristic Design for Unit Commitment Using Large Language Models
Authors: Junjin Lv, Chenggang Cui, Shaodi Zhang, Hui Chen, Chunyang Gong, Jiaming Liu
Date: 2025-06-14 00:00:00
Tasks: Scheduling

The Unit Commitment (UC) problem is a classic challenge in the optimal scheduling of power systems. Years of research and practice have shown that formulating reasonable unit commitment plans can significantly improve the economic efficiency of power systems' operations. In recent years, with the introduction of technologies such as machine learning and the Lagrangian relaxation method, the solution methods for the UC problem have become increasingly diversified, but still face challenges in terms of accuracy and robustness. This paper proposes a Function Space Search (FunSearch) method based on large language models. This method combines pre-trained large language models and evaluators to creatively generate solutions through the program search and evolution process while ensuring their rationality. In simulation experiments, a case of unit commitment with \(10\) units is used mainly. Compared to the genetic algorithm, the results show that FunSearch performs better in terms of sampling time, evaluation time, and total operating cost of the system, demonstrating its great potential as an effective tool for solving the UC problem.
Less Conservative Adaptive Gain-scheduling Control for Continuous-time Systems with Polytopic Uncertainties
Authors: Ariany C. Oliveira, Victor C. S. Campos, Leonardo. A. Mozelli
Date: 2025-06-14 00:00:00
Tasks: Scheduling

The synthesis of adaptive gain-scheduling controller is discussed for continuous-time linear models characterized by polytopic uncertainties. The proposed approach computes the control law assuming the parameters as uncertain and adaptively provides an estimate for the gain-scheduling implementation. Conservativeness is reduced using our recent results on describing uncertainty: i) a structural relaxation that casts the parameters as outer terms and introduces slack variables; and ii) a precise topological representation that describes the mismatch between the uncertainty and its estimate. Numerical examples illustrate a high degree of relaxation in comparison with the state-of-the-art.
Adding links wisely: how an influencer seeks for leadership in opinion dynamics?
Authors: Lingfei Wang, Yu Xing, Yuhao Yi, Ming Cao, Karl H. Johansson
Date: 2025-06-14 00:00:00

This paper investigates the problem of leadership development for an external influencer using the Friedkin-Johnsen (FJ) opinion dynamics model, where the influencer is modeled as a fully stubborn agent and leadership is quantified by social power. The influencer seeks to maximize her social power by strategically adding a limited number of links to regular agents. This optimization problem is shown to be equivalent to maximizing the absorbing probability to the influencer in an augmented Markov chain. The resulting objective function is both monotone and submodular, enabling the use of a greedy algorithm to compute an approximate solution. To handle large-scale networks efficiently, a random walk sampling over the Markov chain is employed to reduce computational complexity. Analytical characterizations of the solution are provided for both low and high stubbornness of regular agents. Specific network topologies are also examined: for complete graphs with rank-one weight matrices, the problem reduces to a hyperbolic 0-1 programmming problem, which is solvable in polynomial time; for symmetric ring graphs with circulant weight matrices and uniform agent stubbornness, the optimal strategy involves selecting agents that are sufficiently dispersed across the network. Numerical simulations are presented for illustration.
Instantaneous Failure, Repair and Mobility Rates for Markov Reliability Systems: A Wind-Farm application
Authors: Guglielmo D'Amico, Filippo Petroni
Date: 2025-06-14 00:00:00
Tasks: Scheduling

The Rate of Occurrence of Failures (ROCOF) is a widely utilized indicator for assessing a system's performance over time, yet it does not fully disclose the instantaneous behavior of a system. This paper introduces new measures to complement the ROCOF, providing a more comprehensive understanding of system reliability, particularly for Markov systems. We define the Rate of Occurrence of Repairs (ROCOR), which quantifies the system's instantaneous tendency to transition from failure to working states, and the Rate of Inoccurrence (ROI), which measures the propensity to remain within the current subset of states (either working or failure) without transitioning out. Explicit expressions for the computation of these rates are derived for Markov systems. Furthermore, a Total Mobility Rate (TMR) is proposed, integrating these individual rates to capture the overall dynamism of the system. The utility of these new indicators is demonstrated through a significant real-world application to wind farm management. The results from the wind farm study show that ROCOR, ROI, and TMR, when used in conjunction with ROCOF, reveal nuanced operational dynamics and reliability characteristics that are not discernible from static measures like Weibull parameters or ROCOF alone. These indicators can distinguish between sites with similar long-term wind profiles by identifying different "reliability logics," such as persistence-driven versus transition-driven behaviors. This enriched, time-dependent perspective provides valuable information for maintenance scheduling, operational strategies, and risk assessment, ultimately enhancing the ability to manage complex systems effectively.
Relative Entropy Regularized Reinforcement Learning for Efficient Encrypted Policy Synthesis
Authors: Jihoon Suh, Yeongjun Jang, Kaoru Teranishi, Takashi Tanaka
Date: 2025-06-14 00:00:00
Tasks: Model-based Reinforcement Learning, Privacy Preserving, Quantization, reinforcement-learning, Reinforcement Learning

We propose an efficient encrypted policy synthesis to develop privacy-preserving model-based reinforcement learning. We first demonstrate that the relative-entropy-regularized reinforcement learning framework offers a computationally convenient linear and ``min-free'' structure for value iteration, enabling a direct and efficient integration of fully homomorphic encryption with bootstrapping into policy synthesis. Convergence and error bounds are analyzed as encrypted policy synthesis propagates errors under the presence of encryption-induced errors including quantization and bootstrapping. Theoretical analysis is validated by numerical simulations. Results demonstrate the effectiveness of the RERL framework in integrating FHE for encrypted policy synthesis.
Similar Formation Control of Multi-Agent Systems over Directed Acyclic Graphs via Matrix-Weighted Laplacian
Authors: Zhipeng Fan, Yujie Xu, Mingyu Fu, Han Sun, Weiqiu Zhang, Heng Zhang
Date: 2025-06-14 00:00:00

This brief proposes a distributed formation control strategy via matrix-weighted Laplacian that can achieve a similar formation in 2-D planar using inter-agent relative displacement measurement. Formation patterns that include translation, rotation, and scaling can be characterized by the null space of the matrix-weighted Laplacian associated with the topological graph. The main contribution of this brief is to extend the similar formation problem of undirected graphs to directed acyclic graphs and provide the necessary algebraic criteria for leader selection. Stability analysis, illustrative examples, and simulation results are provided.
A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis
Authors: Hui Wei, Dong Yoon Lee, Shubham Rohal, Zhizhang Hu, Shiwei Fang, Shijia Pan
Date: 2025-06-13 00:00:00

Foundation models have gained growing interest in the IoT domain due to their reduced reliance on labeled data and strong generalizability across tasks, which address key limitations of traditional machine learning approaches. However, most existing foundation model based methods are developed for specific IoT tasks, making it difficult to compare approaches across IoT domains and limiting guidance for applying them to new tasks. This survey aims to bridge this gap by providing a comprehensive overview of current methodologies and organizing them around four shared performance objectives by different domains: efficiency, context-awareness, safety, and security & privacy. For each objective, we review representative works, summarize commonly-used techniques and evaluation metrics. This objective-centric organization enables meaningful cross-domain comparisons and offers practical insights for selecting and designing foundation model based solutions for new IoT tasks. We conclude with key directions for future research to guide both practitioners and researchers in advancing the use of foundation models in IoT applications.
The Milieu, Science & Logic of Feedback Control
Authors: Robert R. Bitmead
Date: 2025-06-13 00:00:00

'The cardinal sin in control is to believe that the plant is given' Karl Astrom. Astrom, a towering figure of control theory and practice and awardee of the 1993 IEEE Medal of Honor for his work on adaptive control, provides this assessment of the obstinate part of realizing a feedback controller. And yet we are exhorted to rely on solely-data-driven methods of control design skipping the modeling and plant identification phases entirely. What is going on? Whom should we trust? How do we reconcile the implied ease (or indeed avoidance) of modeling with the steely focus on robustness of the control and the capacity of feedback to accommodate uncertainty? This paper seeks to investigate this subject with the objective of appreciating not whom to trust but what are the circumstances where the direct paradigm of control design from any lightly qualified data set provides a sensible way forward. Here is a clue: It depends on the confidence of your contentions about the plant system, the detailed data themselves and your appetite for failure. The paper attempts to segue repeatedly between the broad philosophical context and hard engineering examples. To instantiate ideas and add detail to vagaries, we incorporate a number of examples, each validated by their demonstrated commercial and industrial viability and each terminating with ein Blickwinkel or perspective in German. As David Wallace-Wells poses, before investing hundreds of billions of dollars we really ought to ask what is the trillion-dollar problem which might potentially be solved. By sticking to industrially proven technologies, we hope to delineate what works suitably well in practice or was judged worthy of the risk.
Harvest and Jam: Optimal Self-Sustainable Jamming Attacks against Remote State Estimation
Authors: Yuxing Zhong, Yuzhe Li, Daniel E. Quevedo, Ling Shi
Date: 2025-06-13 00:00:00
Tasks: State Estimation

This paper considers the optimal power allocation of a jamming attacker against remote state estimation. The attacker is self-sustainable and can harvest energy from the environment to launch attacks. The objective is to carefully allocate its attack power to maximize the estimation error at the fusion center. Regarding the attacker's knowledge of the system, two cases are discussed: (i) perfect channel knowledge and (ii) unknown channel model. For both cases, we formulate the problem as a Markov decision process (MDP) and prove the existence of an optimal deterministic and stationary policy. Moreover, for both cases, we develop algorithms to compute the allocation policy and demonstrate that the proposed algorithms for both cases converge to the optimal policy as time goes to infinity. Additionally, the optimal policy exhibits certain structural properties that can be leveraged to accelerate both algorithms. Numerical examples are given to illustrate the main results.
Compositional and Equilibrium-Free Conditions for Power System Stability -- Part II: Method and Application
Authors: Peng Yang, Yifan Su, Xiaoyu Peng, Hua Geng, Feng Liu
Date: 2025-06-13 00:00:00
Tasks: Distributed Computing

This two-part paper proposes a compositional and equilibrium-free approach to analyzing power system stability. In Part I, we have established the stability theory and proposed stability conditions based on the delta dissipativity. In Part II, we focus on methods for applying our theory to complex power grids. We first propose a method to verify the local condition, i.e., delta dissipativity, for heterogeneous devices in power systems. Then, we propose a method to verify the coupling condition based on Alternating Direction Method of Multipliers (ADMM). Finally, we investigate three applications of our theory including stability assessment toward multiple equilibria, stability assessment under varying operating conditions, and a distributed computing framework. Case studies on modified IEEE 9-bus, 39-bus, and 118-bus benchmarks well verified our theory and methods.
Compositional and Equilibrium-Free Conditions for Power System Stability -- Part I: Theory
Authors: Peng Yang, Xiaoyu Peng, Xi Ru, Hua Geng, Feng Liu
Date: 2025-06-13 00:00:00

Traditional centralized stability analysis struggles with scalability in large complex modern power grids. This two-part paper proposes a compositional and equilibrium-free approach to analyzing power system stability. In Part I, we prove that using equilibrium-free local conditions we can certificate system-wide stability of power systems with heterogeneous nonlinear devices and structure-preserving lossy networks. This is built on a recently developed notion of delta dissipativity, which yields local stability conditions without knowing the system-wide equilibrium. As a consequence, our proposed theory can certificate stability of equilibria set rather than single equilibrium. In Part I, we verify our theory and demonstrate promising implications by the single machine single load benchmark, which helps to better explain the compositional and equilibrium-set-oriented stability analysis. Part II of this paper will provide methods for applying our theory to complex power grids, together with case studies across a wide range of system scales. Our results enable a more scalable and adaptable approach to stability analysis. It also sheds light on how to regulate grid-connected devices to guarantee system-wide stability.
Design and Simulation of Vehicle Motion Tracking System using a Youla Controller Output Observation System
Authors: Rongfei Li, Francis Assadian. Iman Soltani
Date: 2025-06-13 00:00:00

This paper presents a novel linear robust Youla controller output observation system for tracking vehicle motion trajectories using a simple nonlinear kinematic vehicle model, supplemented with positional data from a radar sensor. The proposed system operates across the full vehicle trajectory range with only three linear observers, improving upon previous methods that required four nonlinear observers. To ensure smooth transitions between Youla controllers and observers, a switching technique is introduced, preventing bumps during controller changes. The proposed observer system is evaluated through simulations, demonstrating accurate and robust estimation of longitudinal and lateral positions, vehicle orientation, and velocity from sensor measurements during various standard driving maneuvers. Results are provided for different driving scenarios, including lane changes and intersection crossings, where significant changes in vehicle orientation occur. The novelty of this work lies in the first application of a Youla controller output observer for vehicle tracking estimation.
Deception Against Data-Driven Linear-Quadratic Control
Authors: Filippos Fotiadis, Aris Kanellopoulos, Kyriakos G. Vamvoudakis, Ufuk Topcu
Date: 2025-06-13 00:00:00

Deception is a common defense mechanism against adversaries with an information disadvantage. It can force such adversaries to select suboptimal policies for a defender's benefit. We consider a setting where an adversary tries to learn the optimal linear-quadratic attack against a system, the dynamics of which it does not know. On the other end, a defender who knows its dynamics exploits its information advantage and injects a deceptive input into the system to mislead the adversary. The defender's aim is to then strategically design this deceptive input: it should force the adversary to learn, as closely as possible, a pre-selected attack that is different from the optimal one. We show that this deception design problem boils down to the solution of a coupled algebraic Riccati and a Lyapunov equation which, however, are challenging to tackle analytically. Nevertheless, we use a block successive over-relaxation algorithm to extract their solution numerically and prove the algorithm's convergence under certain conditions. We perform simulations on a benchmark aircraft, where we showcase how the proposed algorithm can mislead adversaries into learning attacks that are less performance-degrading.
A Hybrid Adaptive Nash Equilibrium Solver for Distributed Multi-Agent Systems with Game-Theoretic Jump Triggering
Authors: Qiuyu Miao, Zhigang Wu
Date: 2025-06-12 00:00:00
Tasks: Computational Efficiency

This paper presents a hybrid adaptive Nash equilibrium solver for distributed multi-agent systems incorporating game-theoretic jump triggering mechanisms. The approach addresses fundamental scalability and computational challenges in multi-agent hybrid systems by integrating distributed game-theoretic optimization with systematic hybrid system design. A novel game-theoretic jump triggering mechanism coordinates discrete mode transitions across multiple agents while maintaining distributed autonomy. The Hybrid Adaptive Nash Equilibrium Solver (HANES) algorithm integrates these methodologies. Sufficient conditions establish exponential convergence to consensus under distributed information constraints. The framework provides rigorous stability guarantees through coupled Hamilton-Jacobi-Bellman equations while enabling rapid emergency response capabilities through coordinated jump dynamics. Simulation studies in pursuit-evasion and leader-follower consensus scenarios demonstrate significant improvements in convergence time, computational efficiency, and scalability compared to existing centralized and distributed approaches.
Domain-Constrained Diffusion Models to Synthesize Tabular Data: A Case Study in Power Systems
Authors: Milad Hoseinpour, Vladimir Dvorkin
Date: 2025-06-12 00:00:00

Growing concerns over privacy, security, and legal barriers are driving the rising demand for synthetic data across domains such as healthcare, finance, and energy. While generative models offer a promising solution to overcome these barriers, their utility depends on the incorporation of domain-specific knowledge. We propose to synthesize data using a guided diffusion model that integrates domain constraints directly into the generative process. We develop the model in the context of power systems, with potential applicability to other domains that involve tabular data. Specifically, we synthesize statistically representative and high-fidelity power flow datasets. To satisfy domain constraints, e.g., Kirchhoff laws, we introduce a gradient-based guidance to steer the sampling trajectory in a feasible direction. Numerical results demonstrate the effectiveness of our approach.
Smart Predict-Then-Control: Integrating identification and control via decision regret
Authors: Jiachen Li, Shihao Li, Dongmei Chen
Date: 2025-06-12 00:00:00

This paper presents Smart Predict-Then-Control (SPC) framework for integrating system identification and control. This novel SPC framework addresses the limitations of traditional methods, the unaligned modeling error and control cost. It leverages decision regret to prioritize control-relevant dynamics, optimizing prediction errors based on their impact on control performance. Furthermore, the existence of guarantees on regret bounds are theoretically proved. The proposed SPC is validated on both linear and nonlinear systems.
Can Time-Series Foundation Models Perform Building Energy Management Tasks?
Authors: Ozan Baris Mulayim, Pengrui Quan, Liying Han, Xiaomin Ouyang, Dezhi Hong, Mario Bergés, Mani Srivastava
Date: 2025-06-12 00:00:00
Tasks: energy management, Management, Time Series

Building energy management (BEM) tasks require processing and learning from a variety of time-series data. Existing solutions rely on bespoke task- and data-specific models to perform these tasks, limiting their broader applicability. Inspired by the transformative success of Large Language Models (LLMs), Time-Series Foundation Models (TSFMs), trained on diverse datasets, have the potential to change this. Were TSFMs to achieve a level of generalizability across tasks and contexts akin to LLMs, they could fundamentally address the scalability challenges pervasive in BEM. To understand where they stand today, we evaluate TSFMs across four dimensions: (1) generalizability in zero-shot univariate forecasting, (2) forecasting with covariates for thermal behavior modeling, (3) zero-shot representation learning for classification tasks, and (4) robustness to performance metrics and varying operational conditions. Our results reveal that TSFMs exhibit \emph{limited} generalizability, performing only marginally better than statistical models on unseen datasets and modalities for univariate forecasting. Similarly, inclusion of covariates in TSFMs does not yield performance improvements, and their performance remains inferior to conventional models that utilize covariates. While TSFMs generate effective zero-shot representations for downstream classification tasks, they may remain inferior to statistical models in forecasting when statistical models perform test-time fitting. Moreover, TSFMs forecasting performance is sensitive to evaluation metrics, and they struggle in more complex building environments compared to statistical models. These findings underscore the need for targeted advancements in TSFM design, particularly their handling of covariates and incorporating context and temporal dynamics into prediction mechanisms, to develop more adaptable and scalable solutions for BEM.
Conformal Safety Shielding for Imperfect-Perception Agents
Authors: William Scarbro, Calum Imrie, Sinem Getir Yaman, Kavan Fatehi, Corina S. Pasareanu, Radu Calinescu, Ravi Mangal
Date: 2025-06-12 00:00:00
Tasks: Conformal Prediction, State Estimation

We consider the problem of safe control in discrete autonomous agents that use learned components for imperfect perception (or more generally, state estimation) from high-dimensional observations. We propose a shield construction that provides run-time safety guarantees under perception errors by restricting the actions available to an agent, modeled as a Markov decision process, as a function of the state estimates. Our construction uses conformal prediction for the perception component, which guarantees that for each observation, the predicted set of estimates includes the actual state with a user-specified probability. The shield allows an action only if it is allowed for all the estimates in the predicted set, resulting in a local safety guarantee. We also articulate and prove a global safety property of existing shield constructions for perfect-perception agents bounding the probability of reaching unsafe states if the agent always chooses actions prescribed by the shield. We illustrate our approach with a case-study of an experimental autonomous system that guides airplanes on taxiways using high-dimensional perception DNNs.