pwc-archive/papers-with-abstracts huggingface dataset:
Decentralized Optimization on Compact Submanifolds by Quantized Riemannian Gradient Tracking
Authors: Jun Chen, Lina Liu, Tianyi Zhu, Yong liu, Guang Dai, Yunliang Jiang, Ivor W. Tsang
Date: 2025-06-09 00:00:00
Tasks: Distributed Optimization, Quantization

This paper considers the problem of decentralized optimization on compact submanifolds, where a finite sum of smooth (possibly non-convex) local functions is minimized by $n$ agents forming an undirected and connected graph. However, the efficiency of distributed optimization is often hindered by communication bottlenecks. To mitigate this, we propose the Quantized Riemannian Gradient Tracking (Q-RGT) algorithm, where agents update their local variables using quantized gradients. The introduction of quantization noise allows our algorithm to bypass the constraints of the accurate Riemannian projection operator (such as retraction), further improving iterative efficiency. To the best of our knowledge, this is the first algorithm to achieve an $\mathcal{O}(1/K)$ convergence rate in the presence of quantization, matching the convergence rate of methods without quantization. Additionally, we explicitly derive lower bounds on decentralized consensus associated with a function of quantization levels. Numerical experiments demonstrate that Q-RGT performs comparably to non-quantized methods while reducing communication bottlenecks and computational overhead.
Distributed Risk-Sensitive Safety Filters for Uncertain Discrete-Time Systems
Authors: Armin Lederer, Erfaun Noorani, Andreas Krause
Date: 2025-06-09 00:00:00

Ensuring safety in multi-agent systems is a significant challenge, particularly in settings where centralized coordination is impractical. In this work, we propose a novel risk-sensitive safety filter for discrete-time multi-agent systems with uncertain dynamics that leverages control barrier functions (CBFs) defined through value functions. Our approach relies on centralized risk-sensitive safety conditions based on exponential risk operators to ensure robustness against model uncertainties. We introduce a distributed formulation of the safety filter by deriving two alternative strategies: one based on worst-case anticipation and another on proximity to a known safe policy. By allowing agents to switch between strategies, feasibility can be ensured. Through detailed numerical evaluations, we demonstrate the efficacy of our approach in maintaining safety without being overly conservative.
Decentralized Optimization with Amplified Privacy via Efficient Communication
Authors: Wei Huo, Changxin Liu, Kemi Ding, Karl Henrik Johansson, Ling Shi
Date: 2025-06-08 00:00:00

Decentralized optimization is crucial for multi-agent systems, with significant concerns about communication efficiency and privacy. This paper explores the role of efficient communication in decentralized stochastic gradient descent algorithms for enhancing privacy preservation. We develop a novel algorithm that incorporates two key features: random agent activation and sparsified communication. Utilizing differential privacy, we demonstrate that these features reduce noise without sacrificing privacy, thereby amplifying the privacy guarantee and improving accuracy. Additionally, we analyze the convergence and the privacy-accuracy-communication trade-off of the proposed algorithm. Finally, we present experimental results to illustrate the effectiveness of our algorithm.
On the Generalization of Data-Assisted Control in port-Hamiltonian Systems (DAC-pH)
Authors: Mostafa Eslami, Maryam Babazadeh
Date: 2025-06-08 00:00:00
Tasks: parameter estimation, Reinforcement Learning (RL)

This paper introduces a hypothetical hybrid control framework for port-Hamiltonian (p$\mathcal{H}$) systems, employing a dynamic decomposition based on Data-Assisted Control (DAC). The system's evolution is split into two parts with fixed topology: Right-Hand Side (RHS)- an intrinsic Hamiltonian flow handling worst-case parametric uncertainties, and Left-Hand Side (LHS)- a dissipative/input flow addressing both structural and parametric uncertainties. A virtual port variable $\Pi$ serves as the interface between these two components. A nonlinear controller manages the intrinsic Hamiltonian flow, determining a desired port control value $\Pi_c$. Concurrently, Reinforcement Learning (RL) is applied to the dissipative/input flow to learn an agent for providing optimal policy in mapping $\Pi_c$ to the actual system input. This hybrid approach effectively manages RHS uncertainties while preserving the system's inherent structure. Key advantages include adjustable performance via LHS controller parameters, enhanced AI explainability and interpretability through the port variable $\Pi$, the ability to guarantee safety and state attainability with hard/soft constraints, reduced complexity in learning hypothesis classes compared to end-to-end solutions, and improved state/parameter estimation using LHS prior knowledge and system Hamiltonian to address partial observability. The paper details the p$\mathcal{H}$ formulation, derives the decomposition, and presents the modular controller architecture. Beyond design, crucial aspects of stability and robustness analysis and synthesis are investigated, paving the way for deeper theoretical investigations. An application example, a pendulum with nonlinear dynamics, is simulated to demonstrate the approach's empirical and phenomenological benefits for future research.
Attitude Estimation Using Scalar Measurements
Authors: Hassan Alnahhal, Sifeddine Benahmed, Soulaimane Berkane, Tarel Hamel
Date: 2025-06-08 00:00:00

This paper revisits the problem of orientation estimation for rigid bodies through a novel framework based on scalar measurements. Unlike traditional vector-based methods, the proposed approach enables selective utilization of only the reliable axes of vector measurements while seamlessly incorporating alternative scalar modalities such as Pitot tubes, barometers with range sensors, and landmark-based constraints. The estimation problem is reformulated within a linear time-varying (LTV) framework, allowing the application of a deterministic linear Kalman filter. This design guarantees Global Uniform Exponential Stability (GES) under the Uniform Observability (UO) condition. Simulation results demonstrate the effectiveness of the proposed approach in achieving robust and accurate attitude estimation, even with partial vector measurements that simulate sensor axis failure.
Deep reinforcement learning-based joint real-time energy scheduling for green buildings with heterogeneous battery energy storage devices
Authors: Chi Liu, Zhezhuang Xu, Jiawei Zhou, Yazhou Yuan, Kai Ma, Meng Yuan
Date: 2025-06-07 00:00:00
Tasks: Deep Reinforcement Learning, energy management, Scheduling

Green buildings (GBs) with renewable energy and building energy management systems (BEMS) enable efficient energy use and support sustainable development. Electric vehicles (EVs), as flexible storage resources, enhance system flexibility when integrated with stationary energy storage systems (ESS) for real-time scheduling. However, differing degradation and operational characteristics of ESS and EVs complicate scheduling strategies. This paper proposes a model-free deep reinforcement learning (DRL) method for joint real-time scheduling based on a combined battery system (CBS) integrating ESS and EVs. We develop accurate degradation models and cost estimates, prioritize EV travel demands, and enable collaborative ESS-EV operation under varying conditions. A prediction model optimizes energy interaction between CBS and BEMS. To address heterogeneous states, action coupling, and learning efficiency, the DRL algorithm incorporates double networks, a dueling mechanism, and prioritized experience replay. Experiments show a 37.94 percent to 40.01 percent reduction in operating costs compared to a mixed-integer linear programming (MILP) approach.
Adaptive Event-triggered Formation Control of Autonomous Vehicles
Authors: ZiMing Wang, Yihuai Zhang, Chenguang Zhao, Huan Yu
Date: 2025-06-07 00:00:00
Tasks: Autonomous Vehicles

This paper presents adaptive event-triggered formation control strategies for autonomous vehicles (AVs) subject to longitudinal and lateral motion uncertainties. The proposed framework explores various vehicular formations to enable safe and efficient navigation in complex traffic scenarios, such as narrow passages, collaborative obstacle avoidance, and adaptation to cut-in maneuvers. In contrast to conventional platoon control strategies that rely on predefined communication topologies and continuous state transmission, our approach employs a sampling-based observer to reconstruct vehicle dynamics. Building upon an adaptive backstepping continuous-time controller, we design three distinct event-triggered mechanisms, each offering a different trade-off between formation tracking performance and control efficiency by reducing the frequency of control signal updates. A Lyapunov-based stability analysis is conducted to guarantee bounded tracking errors and to avoid Zeno behavior. Finally, the proposed event-triggered controllers are validated through simulations of vehicular formation in three scenarios, highlighting their impact on traffic safety and mobility.
Optimizing Battery and Line Undergrounding Investments for Transmission Systems under Wildfire Risk Scenarios: A Benders Decomposition Approach
Authors: Ryan Piansky, Rahul K. Gupta, Daniel K. Molzahn
Date: 2025-06-07 00:00:00

With electric power infrastructure posing an increasing risk of igniting wildfires under continuing climate change, utilities are frequently de-energizing power lines to mitigate wildfire ignition risk, which can cause load shedding. Recent research advocates for installing battery energy storage systems as well as undergrounding risky overhead lines to reduce the load shedding during such de-energizations. Since wildfire ignition risk can exhibit substantial geographic and temporal variations, it is important to plan battery installation and line undergrounding investments while considering multiple possible scenarios. This paper presents a scenario-based framework for optimizing battery installation and line undergrounding investments while considering many scenarios, each consisting of a day-long time series of uncertain parameters for the load demand, renewable generation, and wildfire ignition risks. This problem is difficult to solve due to a large number of scenarios and binary variables associated with the battery placements as well as the lines to be undergrounded. To address the computational challenges, we decompose the problem in a two-stage scheme via a Benders decomposition approach. The first stage is a master problem formulated as a mixed integer linear programming (MILP) model that makes decisions on the locations and sizes of batteries as well as the lines to be undergrounded. The second stage consists of a linear programming model that assesses these battery and line undergrounding decisions as modeled by a DC OPF formulation. We demonstrate the effectiveness of the proposed scheme on a large-scale transmission network with real world data on wildfire ignition risks, load, and renewable generation.
From Model-Based and Adaptive Control to Evolving Fuzzy Control
Authors: Daniel Leite, Igor Škrjanc, Fernando Gomide
Date: 2025-06-07 00:00:00

Evolving fuzzy systems build and adapt fuzzy models - such as predictors and controllers - by incrementally updating their rule-base structure from data streams. On the occasion of the 60-year anniversary of fuzzy set theory, commemorated during the Fuzz-IEEE 2025 event, this brief paper revisits the historical development and core contributions of classical fuzzy and adaptive modeling and control frameworks. It then highlights the emergence and significance of evolving intelligent systems in fuzzy modeling and control, emphasizing their advantages in handling nonstationary environments. Key challenges and future directions are discussed, including safety, interpretability, and principled structural evolution.
Evaluating Undergrounding Decisions for Wildfire Ignition Risk Mitigation across Multiple Hazards
Authors: Ryan Piansky, Daniel K. Molzahn, Nicole D. Jackson, J. Kyle Skolfield
Date: 2025-06-06 00:00:00

With electric power infrastructure increasingly susceptible to impacts from climate-driven natural disasters, there is an increasing need for optimization algorithms that determine where to harden the power grid. Prior work has primarily developed optimal hardening approaches for specific acute disaster scenarios. Given the extensive costs of hardening the grid, it is important to understand how a particular set of resilience investments will perform under multiple types of natural hazards. Using a large-scale test case representing the Texas power system, this paper aims to understand how line undergrounding investment decisions made for wildfire ignition risk mitigation perform during a range of wildfire, hurricane, and wind events. Given the varying geographical spread and damage profile of these events, we show that investment decisions made to address one type of natural disaster do not necessarily improve broader resilience outcomes, supporting the need for co-optimization across a range of hazards.
Learning Neural Controllers with Optimality and Stability Guarantees Using Input-Output Dissipativity
Authors: Han Wang, Keyan Miao, Diego Madeira, Antonis Papachristodoulou
Date: 2025-06-06 00:00:00

Deep learning methods have demonstrated significant potential for addressing complex nonlinear control problems. For real-world safety-critical tasks, however, it is crucial to provide formal stability guarantees for the designed controllers. In this paper, we propose a new framework for designing neural controllers that achieve both stability and optimality with respect to certain functions. Our key idea is to exploit the concept of input-output dissipativity of nonlinear systems by learning neural storage functions and supply rate functions. As a generalization of Lyapunov theory, dissipativity theory provides a natural connection to optimal control theory, offering both stability guarantees and meaningful optimality certificates. The neural controllers can be directly derived from the learned supply rate functions and guarantee closed-loop stability while inheriting optimality properties that can be shaped towards user-defined control objectives. Extensive numerical experiments demonstrate the effectiveness of our approach.
RIS Size Determination Across Frequencies and Deployment Scenarios: A Simulation-Based Study
Authors: Emre Arslan, Ahmet Faruk Coskun
Date: 2025-06-06 00:00:00

Despite the growing interest in the integration of reconfigurable intelligent surfaces (RIS) into next-generation wireless communications systems, a critical gap remains in understanding what the dimensions of an RIS must be to provide meaningful performance gains across realistic deployment scenarios. This paper addresses this challenge by presenting a practical and scenario-aware methodology for determining optimal RIS dimensions, tailored to specific frequency bands, environments, and use cases. Leveraging a realistic simulation model that incorporates angular scattering characteristics, practical network node locations, and propagation constraints, we evaluate the RIS-assisted performance in a diverse set of configurations. For selected use-cases, we quantify key performance indicators such as average signal-to-noise ratio and outage probability, and we demonstrate how RIS size impacts system reliability. Our findings show that RIS deployment effectiveness is highly sensitive to both physical size and geometric placement, and that there is no one-size-fits-all solution. The proposed framework, supported by detailed use case tables and validated through comprehensive simulations, offers design guidelines for operators and vendors seeking to deploy RIS in practical wireless network settings.
Experimental Performances of mmWave RIS-assisted 5G-Advanced Wireless Deployments in Urban Environments
Authors: Ahmet Faruk Coskun, Alper Tolga Kocaoglu, Emre Arslan, Zehra Yigit, Samed Kesir, Batuhan Kaplan, Jianwu Dou, Yijun Cui
Date: 2025-06-06 00:00:00

Reconfigurable intelligent surface (RIS) has emerged as a groundbreaking technology for 6G wireless communication networks, enabling cost-effective control over wireless propagation environment. By dynamically manipulating its codebook so as to deflect the direction of the reflected electromagnetic wave, RIS can achieve enhanced signal quality, extended coverage, and interference mitigation. This study presents experimental performance of ZTE Dynamic 2.0 RIS products through a series of real-world tests conducted on Turkcell's millimeter-wave (mmWave) testbed. The evaluation involves network coverage extension in urban areas, multi-user efficiency, and the integration of virtual reality technology to support immersive applications in next-generation 6G networks. Through a comprehensive measurement-based analysis, the performance of the RIS product is demonstrated, highlighting its potential to address critical challenges in mmWave communications and to enable advanced 6G use cases.
Hierarchical Debate-Based Large Language Model (LLM) for Complex Task Planning of 6G Network Management
Authors: Yuyan Lin, Hao Zhou, Chengming Hu, Xue Liu, Hao Chen, Yan Xin, Jianzhong, Zhang
Date: 2025-06-06 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Management, Task Planning

6G networks have become increasingly complicated due to novel network architecture and newly emerging signal processing and transmission techniques, leading to significant burdens to 6G network management. Large language models (LLMs) have recently been considered a promising technique to equip 6G networks with AI-native intelligence. Different from most existing studies that only consider a single LLM, this work involves a multi-LLM debate-based scheme for 6G network management, where multiple LLMs can collaboratively improve the initial solution sequentially. Considering the complex nature of 6G domain, we propose a novel hierarchical debate scheme: LLMs will first debate the sub-task decomposition, and then debate each subtask step-by-step. Such a hierarchical approach can significantly reduce the overall debate difficulty by sub-task decomposition, aligning well with the complex nature of 6G networks and ensuring the final solution qualities. In addition, to better evaluate the proposed technique, we have defined a novel dataset named 6GPlan, including 110 complex 6G network management tasks and 5000 keyword solutions. Finally, the experiments show that the proposed hierarchical debate can significantly improve performance compared to baseline techniques, e.g. more than 30% coverage rate and global recall rate improvement.
The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage
Authors: Manuel Sage, Khalil Al Handawi, Yaoyao Fiona Zhao
Date: 2025-06-06 00:00:00
Tasks: Deep Reinforcement Learning

Power-to-Gas (P2G) technologies gain recognition for enabling the integration of intermittent renewables, such as wind and solar, into electricity grids. However, determining the most cost-effective operation of these systems is complex due to the volatile nature of renewable energy, electricity prices, and loads. Additionally, P2G systems are less efficient in converting and storing energy compared to battery energy storage systems (BESs), and the benefits of converting electricity into gas are not immediately apparent. Deep Reinforcement Learning (DRL) has shown promise in managing the operation of energy systems amidst these uncertainties. Yet, DRL techniques face difficulties with the delayed reward characteristic of P2G system operation. Previous research has mostly focused on short-term studies that look at the energy conversion process, neglecting the long-term storage capabilities of P2G. This study presents a new method by thoroughly examining how DRL can be applied to the economic operation of P2G systems, in combination with BESs and gas turbines, over extended periods. Through three progressively more complex case studies, we assess the performance of DRL algorithms, specifically Deep Q-Networks and Proximal Policy Optimization, and introduce modifications to enhance their effectiveness. These modifications include integrating forecasts, implementing penalties on the reward function, and applying strategic cost calculations, all aimed at addressing the issue of delayed rewards. Our findings indicate that while DRL initially struggles with the complex decision-making required for P2G system operation, the adjustments we propose significantly improve its capability to devise cost-effective operation strategies, thereby unlocking the potential for long-term energy storage in P2G technologies.
Continuous-Time Output Feedback Adaptive Control for Stabilization and Tracking with Experimental Results
Authors: Mohammad Mirtaba, Ankit Goel
Date: 2025-06-06 00:00:00

This paper presents a continuous-time output feedback adaptive control technique for stabilization and tracking control problems. The adaptive controller is motivated by the classical discrete-time retrospective cost adaptive control algorithm. The particle swarm optimization framework automates the adaptive algorithm's hyper-parameter tuning. The proposed controller is numerically validated in the tracking problems of a double integrator and a bicopter system and is experimentally validated in an attitude stabilization problem. Numerical and experimental results show that the proposed controller is an effective technique for model-free output feedback control.
A Koopman-backstepping approach to data-driven robust output regulation for linear parabolic systems
Authors: Joachim Deutscher, Julian Zimmer
Date: 2025-06-06 00:00:00

In this paper a solution of the data-driven robust output regulation problem for linear parabolic systems is presented. Both the system as well as the ODE, i.e., the disturbance model, describing the disturbances are unknown, but finite-time sequential data obtained from measurements of the output to be controlled and additional boundary outputs are available. The data-driven controller is designed in the Koopman operator framework for PDEs, where the Koopman modes and eigenvalues are obtained from data using Hankel-DMD. It is shown that all system parameters and the eigenvalues of the disturbance model can be recovered from the available measurements by solving an inverse Sturm-Liouville problem. This allows to directly apply backstepping methods for the robust regulator design. For this, closed-loop stability in the presence of small errors in the Hankel-DMD is verified in the nominal case. Robust output regulation is shown for non-destabilizing model uncertainties. A numerical example demonstrates the results of the paper.
Data-driven nonlinear output regulation via data-enforced incremental passivity
Authors: Yixuan Liu, Meichen Guo
Date: 2025-06-06 00:00:00

This work proposes a data-driven regulator design that drives the output of a nonlinear system asymptotically to a time-varying reference and rejects time-varying disturbances. The key idea is to design a data-driven feedback controller such that the closed-loop system is incrementally passive with respect to the regulation error and a virtual input. By carefully designing the virtual input, we solve the data-driven nonlinear output regulation problem where the reference and disturbances are generated by a linear exosystem. The designed regulator is composed of an internal model and a passivation feedback controller characterized by a set of data-dependent linear matrix inequalities. The proposed data-driven method is also applied to stabilizing the non-zero equilibrium of a class of nonlinear systems with unknown equilibrium input. Numerical examples are presented to illustrate the effectiveness of the proposed designs.