pwc-archive/papers-with-abstracts huggingface dataset:
AgenticControl: An Automated Control Design Framework Using Large Language Models
Authors: Mohammad Narimani, Seyyed Ali Emami
Date: 2025-06-23 00:00:00
Tasks: In-Context Learning, Large Language Model, Model Predictive Control

Traditional control system design, reliant on expert knowledge and precise models, struggles with complex, nonlinear, or uncertain dynamics. This paper introduces AgenticControl, a novel multi-agent framework that automates controller design using coordinated Large Language Model (LLM) agents. Through structured JSON communication, these agents handle tasks including controller selection, scenario design, parameter optimization, performance evaluation, and decision-making. Through an actor-critic optimization approach, the system iteratively improves performance while progressing through scenarios of increasing complexity to ensure robustness under nominal conditions, measurement noise, actuator disturbances, and parametric uncertainties. Key innovations include structured multi-agent collaboration, robust optimization mechanisms, and real-time adaptability via in-context learning. Validated across four diverse control systems, namely, DC Motor Position control, Ball and Beam, Inverted Pendulum, and Double Inverted Pendulum, the framework achieves competitive performance against classical methods. Its Full State Feedback solution closely matches Linear Quadratic Regulator (LQR) results, while the designed PID controller significantly outperforming MATLAB's PIDTuner, reducing PID tracking error by 55% through adaptive parameter exploration. A comparative study of five LLM models reveals distinct optimization profiles, with DeepSeek achieving the fastest convergence. This work demonstrates the potential of LLM-driven control design, paving the way for advanced techniques like model predictive control and reinforcement learning.
Looking for Signs: Reasoning About FOBNNs Using SAT
Authors: Hans-Jörg Schurr, Athénaïs Vaginay
Date: 2025-06-23 00:00:00

First-Order Boolean Networks with Non-deterministic updates (FOBNN) compute a boolean transition graph representing the absence and presence of species over time. The utility of FOBNNs has been justified by their theoretical soundness with respect to the Euler simulation of the differential equations. However, we lack practical means to work with FOBNNs and an empirical evaluation of their properties. We present a sound and efficient reduction of the first-order FOBNN transition relation to a propositional logic formula. This makes it possible to use modern SAT solvers to reason on the full transition graph, even for large models. We use this encoding to assess the feasibility and efficiency of practical reasoning with FOBNNs. To do so, we focus on the computation of fixed points. We also compare the transition graphs obtained via FOBNNs to those computed by the classic boolean semantics of reaction networks. Overall, our encoding opens new directions for the analysis of FOBNNs and deepens the understanding of their relationship with reaction networks.
Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning
Authors: Mehmet Fatih Ozkan, Samuel Filgueira da Silva, Faissal El Idrissi, Prashanth Ramesh, Marcello Canova
Date: 2025-06-23 00:00:00
Tasks: Deep Reinforcement Learning, Experimental Design, Model Predictive Control, parameter estimation

Accurate parameter estimation in electrochemical battery models is essential for monitoring and assessing the performance of lithium-ion batteries (LiBs). This paper presents a novel approach that combines deep reinforcement learning (DRL) with an optimal experimental design (OED) framework to identify key electrochemical parameters of LiB cell models. The proposed method utilizes the twin delayed deep deterministic policy gradient (TD3) algorithm to optimize input excitation, thereby increasing the sensitivity of the system response to electrochemical parameters. The performance of this DRL-based approach is evaluated against a nonlinear model predictive control (NMPC) method and conventional tests. Results indicate that the DRL-based method provides superior information content, reflected in higher Fisher information (FI) values and lower parameter estimation errors compared to the NMPC design and conventional test practices. Additionally, the DRL approach offers a substantial reduction in experimental time and computational resources.
Model Reference Adaptive Control of Networked Systems with State and Input Delays
Authors: Moh Kamalul Wafi, Katherin Indriawati, Bambang L. Widjiantoro
Date: 2025-06-23 00:00:00

Adaptive control strategies have progressively advanced to accommodate increasingly uncertain, delayed, and interconnected systems. This paper addresses the model reference adaptive control (MRAC) of networked, heterogeneous, and unknown dynamical agents subject to both state and input delays. The objective is to ensure that all follower agents asymptotically track the trajectory of a stable leader system, despite system uncertainties and communication constraints. Two communication topologies are considered, full connectivity between each agent and the leader, and partial connectivity wherein agents rely on both neighboring peers and the leader. The agent-to-agent and agent-to-leader interactions are encoded using a Laplacian-like matrix and a diagonal model-weighting matrix, respectively. To compensate for the delays, a predictor-based control structure and an auxiliary dynamic system are proposed. The control framework includes distributed adaptive parameter laws derived via Lyapunov-based analysis, ensuring convergence of the augmented tracking error. Stability conditions are established through a carefully constructed Lyapunov Krasovskii functional, under minimal assumptions on connectivity and excitation. Numerical simulations of both network structures validate the proposed method, demonstrating that exact leader tracking is achieved under appropriately designed learning rates and initializations. This work lays a foundation for future studies on fault-resilient distributed adaptive control incorporating data-driven or reinforcement learning techniques.
PGLib-CO2: A Power Grid Library for Computing and Optimizing Carbon Emissions
Authors: Young-ho Cho, Min-Seung Ko, Hao Zhu
Date: 2025-06-17 00:00:00
Tasks: Benchmarking

A sustainable electricity infrastructure requires the explicit integration of carbon emissions into power system modeling and optimization paradigms. However, existing open-source datasets for power system R&D lack generator-level carbon emission profiling, limiting the ability to benchmark and compare various carbon-aware grid operational strategies. To address this gap, this work introduces PGLib-CO2, an open-source extension to the widely adopted PGLib-OPF test case library. PGLib-CO2 enriches standard network cases with CO2 and CO2-equivalent emission intensity factors by expanding the fuel-type categorization used by PGLib-OPF, attaining a realistic generator-level carbon profiling. It is also packaged for both Python's pandapower and Julia's PowerModels.jl, for a seamless, user-friendly integration of emission modeling into grid computation and optimization tasks. The dataset produced by PGLib-CO2 can support grid-based carbon accounting, emission metric evaluation, and integration into AC optimal power flow (OPF) and optimal load shifting (OLS) formulations. We demonstrate PGLib-CO2's utility through case studies that quantify cost-emission trade-offs and optimize a carbon-aware objective function. By standardizing carbon-enhanced test cases, PGLib-CO2 provides an open-source, reproducible foundation for benchmarking carbon-aware computation, facilitating future research in sustainable power system operation.
Steering Conceptual Bias via Transformer Latent-Subspace Activation
Authors: Vansh Sharma, Venkat Raman
Date: 2025-06-23 00:00:00
Tasks: Code Generation

This work examines whether activating latent subspaces in language models (LLMs) can steer scientific code generation toward a specific programming language. Five causal LLMs were first evaluated on scientific coding prompts to quantify their baseline bias among four programming languages. A static neuron-attribution method, perturbing the highest activated MLP weight for a C++ or CPP token, proved brittle and exhibited limited generalization across prompt styles and model scales. To address these limitations, a gradient-refined adaptive activation steering framework (G-ACT) was developed: per-prompt activation differences are clustered into a small set of steering directions, and lightweight per-layer probes are trained and refined online to select the appropriate steering vector. In LLaMA-3.2 3B, this approach reliably biases generation towards the CPP language by increasing the average probe classification accuracy by 15% and the early layers (0-6) improving the probe classification accuracy by 61.5% compared to the standard ACT framework. For LLaMA-3.3 70B, where attention-head signals become more diffuse, targeted injections at key layers still improve language selection. Although per-layer probing introduces a modest inference overhead, it remains practical by steering only a subset of layers and enables reproducible model behavior. These results demonstrate a scalable, interpretable and efficient mechanism for concept-level control for practical agentic systems.
Spectrum Opportunities for the Wireless Future: From Direct-to-Device Satellite Applications to 6G Cellular
Authors: Theodore S. Rappaport, Todd E. Humphreys, Shuai Nie
Date: 2025-06-23 00:00:00

For the next-generation wireless networks and beyond, both the upper mid-band (7 GHz-24 GHz) and terahertz (100 GHz-1 THz) spectra are gaining global attention from service providers, academic research groups, policy makers, and standards organizations. This article provides an in-depth analysis of recent regulatory rulings and spectrum preferences issued by international standard bodies such as the International Telecommunications Union and Federal Communications Commission as they seek to identify feasible bands for future wireless networks. In this paper, we present the promising spectrum allocations earmarked for 6G and beyond. We also provide exemplars that illuminate the passive service protections and spectrum feasibility for coexistence between terrestrial wireless networks and satellites and other non-terrestrial networks (NTN), and discuss key technical constraints that will challenge future spectrum use for the wireless industry. The findings highlight promising frequency bands while addressing regulatory and technological challenges for future wireless service deployment.
Hybrid Single-Pulse and Sawyer-Tower Method for Accurate Transistor Loss Separation in High-Frequency High-Efficiency Power Converters
Authors: Xiaoyang Tian, Mowei Lu, Florin Udrea, Stephan Goetz
Date: 2025-06-23 00:00:00

Accurate measurement of transistor parasitic capacitance and its associated energy losses is critical for evaluating device performance, particularly in high-frequency and high-efficiency power conversion systems. This paper proposes a hybrid single-pulse and Sawyer-Tower test method to analyse switching characteristics of field-effect transistors (FET), which not only eliminates overlap losses but also mitigates the effects of current backflow observed in traditional double-pulse testing. Through a precise loss separation model, it enables an accurate quantification of switching losses and provides a refined understanding of device energy dissipation mechanisms. We validate the hysteresis data and loss separation results through experimental measurements on a 350-W LLC converter, which further offers deeper insights into transistor dynamic behaviour and its dependence on operating conditions. This method is applicable to a wide range of transistors, including emerging SiC and GaN devices, and serves as a valuable tool for device characterization and optimization in power electronics.
How flexible do we need to be? Using electricity systems models to identify optimal designs for flexible carbon capture storage system for gas-fired power plants
Authors: Fangwei Cheng, Qian Luo, Jesse Jenkins
Date: 2025-06-23 00:00:00

As the share of variable renewable energy in power systems grows, enhancing the operational flexibility of combined cycle gas turbines with carbon capture and storage (CCGT-CCS) becomes increasingly valuable. This study integrates techno-economic analysis with capacity expansion modeling to quantify the value of improved CCGT-CCS flexibility-such as lower start-up costs, reduced minimum generation, faster ramping, and shorter up/down times-at both plant and system levels. Using the Texas power system as a case study, we find that increased flexibility raises CCGT-CCS generation profits and installed capacity. Under various policy scenarios, CCGT-CCS benefits most from a CO2 tax (or equivalent emissions cap), more so than from clean energy standards or capture subsidies like the federal 45Q tax credit. However, electricity system cost savings remain modest, reducing total costs by only 0.3-0.5%. Thus, flexibility improvements should be pursued only if they entail limited increases in capital and maintenance costs.
New Power Decoupling Method for Grid Forming Inverter Based on Adaptive Virtual-Synchronous Machine in Weak Grids
Authors: Waleed Breesam, Stefan M. Goetz
Date: 2025-06-23 00:00:00

Many countries' policies have shifted rapidly towards using renewable energy for climate reasons. As a result, inverter-based resources are beginning to dominate power systems. Key elements for managing the loss of conventional generators are virtual-synchronous-generator-based grid-forming inverters. Despite the unique advantages of this technology, there are still various challenges, most notably the problem of active-reactive power coupling due to a nonzero power angle and high grid impedance ratio R/X. The effect of power coupling means that any change in the inverter's active power will affect the reactive power and vice versa. This challenge results in grid instability, reduces control performance, and restricts the active power delivery capability of the inverter to the grid. This paper presents a new vision to solve this impact in weak grids by a new power-decoupling method based on adaptive virtual synchronous generator parameters. The power coupling will be studied considering the parameters causing this effect. Fuzzy logic will serve to adjust the parameters of power control loops. Hardware-in-the-loop testing on a real-time simulator (OP4610) and a physical microcontroller verified and validated the proposed method. The results showed the proposed method's effectiveness in eliminating static and dynamic power coupling and improving the grid-forming inverter performance under different operating conditions.
Frequency Control in Microgrids: An Adaptive Fuzzy-Neural-Network Virtual Synchronous Generator
Authors: Waleed Breesam, Rezvan Alamian, Nima Tashakor, Brahim Elkhalil Youcefa, Stefan M. Goetz
Date: 2025-06-23 00:00:00

The reliance on distributed renewable energy has increased recently. As a result, power electronic-based distributed generators replaced synchronous generators which led to a change in the dynamic characteristics of the microgrid. Most critically, they reduced system inertia and damping. Virtual synchronous generators emulated in power electronics, which mimic the dynamic behaviour of synchronous generators, are meant to fix this problem. However, fixed virtual synchronous generator parameters cannot guarantee a frequency regulation within the acceptable tolerance range. Conversely, a dynamic adjustment of these virtual parameters promises robust solution with stable frequency. This paper proposes a method to adapt the inertia, damping, and droop parameters dynamically through a fuzzy neural network controller. This controller trains itself online to choose appropriate values for these virtual parameters. The proposed method can be applied to a typical AC microgrid by considering the penetration and impact of renewable energy sources. We study the system in a MATLAB/Simulink model and validate it experimentally in real time using hardware-in-the-loop based on an embedded ARM system (SAM3X8E, Cortex-M3). Compared to traditional and fuzzy logic controller methods, the results demonstrate that the proposed method significantly reduces the frequency deviation to less than 0.03 Hz and shortens the stabilizing/recovery time.
A detailed simulation model for fifth generation district heating and cooling networks with seasonal latent storage evaluated on field data
Authors: Manuel Kollmar, Adrian Bürger, Markus Bohlayer, Angelika Altmann-Dieses, Marco Braun, Moritz Diehl
Date: 2025-06-23 00:00:00

Fifth generation district heating and cooling (5GDHC) networks accelerate the use of renewable energies in the heating sector and enable flexible, efficient and future-proof heating and cooling supply via a single network. Due to their low temperature level and high integration of renewables, 5GDHC systems pose new challenges for the modeling of these networks in order to simulate and test operational strategies. A particular feature is the use of uninsulated pipes, which allow energy exchange with the surrounding ground. Accurate modeling of this interaction is essential for reliable simulation and optimization. This paper presents a thermp-physical model of the pip connections, the surrounding soil, a latent heat storage in the form of an ice storage as a seasonal heat storage and the house transfer stations. The model is derived from mass and energy balances leading to ordinary differential equations (ODEs). Validation is performed using field date from the 5GDHC network in Gutach-Bleibach, Germany, which supplies heating and cooling to 30 modern buildings. With an average model deviation of 4.5 % in the normalized mean bias error (NMBE) and 15.9 % in the coefficient of the variation of the root mean square error (CVRMSE), the model's accuracy is validated against the available temperature measurements. The realistic representation of the thermal-hydraulic interactions between soil and pipes, as well as the heat flow within the network, confirms the accuracy of the model and its applicability for the simulation of 5GDHC systems. The model is made openly accessible under an open-source license.
Discrete-Time Linear Dynamical System Control Using Sparse Inputs With Time-Varying Support
Authors: Krishna Praveen V. S. Kondapi, Chandrasekhar Sriram, Geethu Joseph, Chandra R. Murthy
Date: 2025-06-23 00:00:00

In networked control systems, communication resource constraints often necessitate the use of \emph{sparse} control input vectors. A prototypical problem is how to ensure controllability of a linear dynamical system when only a limited number of actuators (inputs) can be active at each time step. In this work, we first present an algorithm for determining the \emph{sparse actuator schedule}, i.e., the sequence of supports of the input vectors that ensures controllability. Next, we extend the algorithm to minimize the average control energy by simultaneously minimizing the trace of the controllability Gramian, under the sparsity constraints. We derive theoretical guarantees for both algorithms: the first algorithm ensures controllability with a minimal number of control inputs at a given sparsity level; for the second algorithm, we derive an upper bound on the average control energy under the resulting actuator schedule. Finally, we develop a novel sparse controller based on Kalman filtering and sparse signal recovery that drives the system to a desired state in the presence of process and measurement noise. We also derive an upper bound on the steady-state MSE attained by the algorithm. We corroborate our theoretical results using numerical simulations and illustrate that sparse control achieves a control performance comparable to the fully actuated systems.
Networked pointing system: Bearing-only target localization and pointing control
Authors: Shiyao Li, Bo Zhu, Yining Zhou, Jie Ma, Baoqing Yang, Fenghua He
Date: 2025-06-23 00:00:00

In the paper, we formulate the target-pointing consensus problem where the headings of agents are required to point at a common target. Only a few agents in the network can measure the bearing information of the target. A two-step solution consisting of a bearing-only estimator for target localization and a control law for target pointing is constructed to address this problem. Compared to the strong assumptions of existing works, we only require two agents not collinear with the target to ensure localizability. By introducing the concept of virtual fusion node, we prove that both the estimation error and the tracking error converge asymptotically to the origin. The video demonstration of the verification can be found at https://youtu.be/S9- eyofk1DY.
Receding Horizon Recursive Location Estimation
Authors: Xu Weng, K. V. Ling, Ling Zhao
Date: 2025-06-23 00:00:00

This paper presents a recursive solution to the receding or moving horizon estimation (MHE) problem for nonlinear time-variant systems. We provide the conditions under which the recursive MHE is equivalent to the extended Kalman filter (EKF), regardless of the horizon size. Theoretical and empirical evidence is also provided. Moreover, we clarify the connection between MHE and factor graph optimization (FGO). We apply the recursive MHE to GNSS localization and evaluate its performance using publicly available datasets. The paper is based on the deterministic least squares framework.
Aperiodic-sampled neural network controllers with closed-loop stability verifications (extended version)
Authors: Renjie Ma, Zhijian Hu, Rongni Yang, Ligang Wu
Date: 2025-06-23 00:00:00

In this paper, we synthesize two aperiodic-sampled deep neural network (DNN) control schemes, based on the closed-loop tracking stability guarantees. By means of the integral quadratic constraint coping with the input-output behaviour of system uncertainties/nonlinearities and the convex relaxations of nonlinear DNN activations leveraging their local sector-bounded attributes, we establish conditions to design the event- and self-triggered logics and to compute the ellipsoidal inner approximations of region of attraction, respectively. Finally, we perform a numerical example of an inverted pendulum to illustrate the effectiveness of the proposed aperiodic-sampled DNN control schemes.
Multi-Task Lifelong Reinforcement Learning for Wireless Sensor Networks
Authors: Hossein Mohammadi Firouzjaei, Rafaela Scaciota, Sumudu Samarakoon
Date: 2025-06-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Enhancing the sustainability and efficiency of wireless sensor networks (WSN) in dynamic and unpredictable environments requires adaptive communication and energy harvesting strategies. We propose a novel adaptive control strategy for WSNs that optimizes data transmission and EH to minimize overall energy consumption while ensuring queue stability and energy storing constraints under dynamic environmental conditions. The notion of adaptability therein is achieved by transferring the known environment-specific knowledge to new conditions resorting to the lifelong reinforcement learning concepts. We evaluate our proposed method against two baseline frameworks: Lyapunov-based optimization, and policy-gradient reinforcement learning (RL). Simulation results demonstrate that our approach rapidly adapts to changing environmental conditions by leveraging transferable knowledge, achieving near-optimal performance approximately $30\%$ faster than the RL method and $60\%$ faster than the Lyapunov-based approach. The implementation is available at our GitHub repository for reproducibility purposes [1].