pwc-archive/papers-with-abstracts huggingface dataset:
On the input admittance of a universal power synchronization controller with droop controllers
Authors: Orcun Karaca, Irina Subotic, Lennart Harnefors, Ioannis Tsoumas
Date: 2025-06-20 00:00:00
Tasks: Sensitivity

Recent work has proposed a universal framework that integrates the well-established power synchronization control into vector current control. Using this controller for the parallel operation of grid-forming converters, and/or with loads that have strong voltage magnitude sensitivity, requires additional loops manipulating the voltage magnitude, e.g., $QV$ and $PV$ voltage-power droop controllers. This paper derives the input admittance of the resulting overall scheme. Sensitivity analyses based on the passivity index demonstrate the benefits of the proportional components of $QV$ and $PV$ control. A case study is presented where a grid-forming converter is operated in parallel with a generator.
Trajectory tracking control of USV with actuator constraints in the presence of disturbances
Authors: Ram Milan Kumar Verma, Shashi Ranjan Kumar, Hemendra Arya
Date: 2025-06-20 00:00:00

All practical systems often pose a problem of finite control capability, which can notably degrade the performance if not properly addressed. Since actuator input bounds are typically known, integrating actuator saturation considerations into the control law design process can lead to enhanced performance and more precise trajectory tracking. Also, the actuators cannot provide the demanded forces or torques instantaneously; hence, there is a limitation on the rate of magnitude. This work proposes nonlinear feedback controller designs developed using the Lyapunov stability and backstepping method while actively considering the actuator magnitude and rate constraints. The system dynamics are augmented with a smooth control input saturation model. Additionally, an observer is incorporated to estimate the disturbance vector. Through Lyapunov stability analysis, we demonstrate the system's stability under the proposed controller for the Uncrewed Surface Vessel (USV), ensuring adherence to actuator constraints provided their initial values fall within the prescribed bounds. Extensive numerical simulations performed by considering various trajectories and multiple initial conditions demonstrate the effectiveness of the controller in maintaining tracking performance without violating actuator constraints. This work also relaxes the assumption of equally capable actuators to be used to control the motion of USVs, affirming the viability of the controller in practical applications.
Distributed Affine Formation Control of Linear Multi-agent Systems with Adaptive Event-triggering
Authors: Chenjun Liu, Jason J. R. Liu, Zhan Shu, James Lam
Date: 2025-06-20 00:00:00

Concerning general multi-agent systems with limited communication, this paper proposes distributed formation control protocols under adaptive event-triggered schemes to operate affine transformations of nominal formations. To accommodate more practical system mechanics, we develop an event-triggered controller that drives the leader to a desired state by bringing in the compensation term. Based on triggering instants' state information, an affine formation control method with adaptive event-triggering is designed for each follower, making the whole protocol effective in refraining from successive communication while not relying on predefined global information. In particular, mitigating the effect of partial state availability, an output-based control solution is presented to expand the protocol's serviceable range. Finally, we perform numerical simulations on the formation and its affine transformations to verify the effectiveness of the control protocol and the feasibility of the event-triggered mechanism.
Power Handling Improvement in Cross-Sectional Lame Mode Resonators Operating in the Ku-band
Authors: Luca Spagnuolo, Gabriel Giribaldi, Filippo Perli, Alberto Corigliano, Luca Colombo, Matteo Rinaldi
Date: 2025-06-19 00:00:00

This study presents power handling improvements in cross-sectional Lame-Mode Resonators (CLMRs) designed for operation in the Ku-band. Previously fabricated CLMR devices failed at approximately 8 dBm of input power, primarily due to electromigration in the aluminum interdigitated electrodes (IDTs). To better understand this mechanism in CLMRs, a data driven thermal model is developed to analyze localized heating effects within the resonator body, which are known to accelerate electromigration. Based on insights from this model, Aluminum Silicon Copper (AlSiCu) was selected for the IDTs due to its superior thermal stability and resistance to electromigration. Devices fabricated with AlSiCu exhibited no signs of performance degradation, with the best-performing resonator achieving a mechanical quality factor (Qm) of 360, a maximum Bode quality factor (QBode) of 500, and an electromechanical coupling coefficient (kt2) of 6.3%. Moreover, the use of AlSiCu significantly increased the maximum input power the device can withstand, showing an improvement of up to 6 dBm over previous devices. These improvements in power handling make the devices strong candidates for high-power Ku-band filtering applications.
Local Differential Privacy for Distributed Stochastic Aggregative Optimization with Guaranteed Optimality
Authors: Ziqin Chen, Yongqiang Wang
Date: 2025-06-18 00:00:00

Distributed aggregative optimization underpins many cooperative optimization and multi-agent control systems, where each agent's objective function depends both on its local optimization variable and an aggregate of all agents' optimization variables. Existing distributed aggregative optimization approaches typically require access to accurate gradients of the objective functions, which, however, are often hard to obtain in real-world applications. For example, in machine learning, gradients are commonly contaminated by two main sources of noise: the randomness inherent in sampled data, and the additional variability introduced by mini-batch computations. In addition to the issue of relying on accurate gradients, existing distributed aggregative optimization approaches require agents to share explicit information, which could breach the privacy of participating agents. We propose an algorithm that can solve both problems with existing distributed aggregative optimization approaches: not only can the proposed algorithm guarantee mean-square convergence to an exact optimal solution when the gradients are subject to noise, it also simultaneously ensures rigorous differential privacy, with the cumulative privacy budget guaranteed to be finite even when the number of iterations tends to infinity. To the best of our knowledge, this is the first algorithm able to guarantee both accurate convergence and rigorous differential privacy in distributed aggregative optimization. Besides characterizing the convergence rates under nonconvex/convex/strongly convex conditions, we also rigorously quantify the cost of differential privacy in terms of convergence rates. Experimental results on personalized machine learning using benchmark datasets confirm the efficacy of the proposed algorithm.
Emission-Aware Operation of Electrical Energy Storage Systems
Authors: Haotian Yao, Vahid Hakimian, Mostafa Farrokhabadi, Hamidreza Zareipour
Date: 2025-06-19 00:00:00

Since the beginning of this century, there has been a growing body of research and developments supporting the participation of energy storage systems (ESS) in the emission reduction mandates. However, regardless of these efforts and despite the need for an accelerated energy transition, we have yet to see a practical framework for operational carbon accounting and credit trading for energy storage systems. In this context, this paper proposes an emission performance credits (EPCs) framework that allows ESS, down to the prosumer level, to participate in the carbon market. Thus, a mechanism is proposed, for the first time, to calculate the grid's real-time marginal emission intensity (MEI). The MEI is then used to optimize the cumulative operational emission of ESS through carbon-aware dispatch. Consequently, the framework tracks the operational emissions and converts them into EPCs, which are then sold to regulated entities under compliance programs. Simulation results support the potential of ESS, regardless of their size, to participate in the broader carbon mitigation objectives.
State-Space Kolmogorov Arnold Networks for Interpretable Nonlinear System Identification
Authors: Gonçalo Granjal Cruz, Balazs Renczes, Mark C Runacres, Jan Decuyper
Date: 2025-06-19 00:00:00
Tasks: Kolmogorov-Arnold Networks

While accurate, black-box system identification models lack interpretability of the underlying system dynamics. This paper proposes State-Space Kolmogorov-Arnold Networks (SS-KAN) to address this challenge by integrating Kolmogorov-Arnold Networks within a state-space framework. The proposed model is validated on two benchmark systems: the Silverbox and the Wiener-Hammerstein benchmarks. Results show that SS-KAN provides enhanced interpretability due to sparsity-promoting regularization and the direct visualization of its learned univariate functions, which reveal system nonlinearities at the cost of accuracy when compared to state-of-the-art black-box models, highlighting SS-KAN as a promising approach for interpretable nonlinear system identification, balancing accuracy and interpretability of nonlinear system dynamics.
Detailed Small-Signal Stability Analysis of the Cigré High-Voltage Network Penetrated by Grid-Following Inverter-Based Resources
Authors: Francesco Conte, Fernando Mancilla-David, Amritansh Sagar, Chendan Li, Federico Silvestro, Samuele Grillo
Date: 2025-06-19 00:00:00

This paper presents a detailed small-signal stability analysis of a modified version of the Cigr\'e European high-voltage network, where one of the synchronous generators is replaced by a grid-following inverter-based resource (IBR). The analysis focuses on the influence of the parameters defining the grid-following IBR control scheme on the stability of the system. Given a set of potential grid configurations and the value of the IBR control parameters, stability is verified by the direct eigenvalue analysis of a high-detailed linearized model of the overall Cigr\'e network. Starting from this procedure, we propose an adaptive sampling method for training a support vector machine classifier able to estimate the probability of stability of the power system over a domain defined by candidate intervals of the considered parameters. The training of the classifier is refined to identify with more accuracy the boundaries of the parameters' stability regions. The obtained results are then compared with those obtained by representing the grid with the classical Th\'evenin equivalent. Results suggest that, when the Th\'evenin equivalent is accurate, the predicted stability region is conservative yet contained within that of the full network.
Intelligent Operation and Maintenance and Prediction Model Optimization for Improving Wind Power Generation Efficiency
Authors: Xun Liu, Xiaobin Wu, Jiaqi He, Rajan Das Gupta
Date: 2025-06-19 00:00:00
Tasks: Data Integration, Model Optimization

This study explores the effectiveness of predictive maintenance models and the optimization of intelligent Operation and Maintenance (O&M) systems in improving wind power generation efficiency. Through qualitative research, structured interviews were conducted with five wind farm engineers and maintenance managers, each with extensive experience in turbine operations. Using thematic analysis, the study revealed that while predictive maintenance models effectively reduce downtime by identifying major faults, they often struggle with detecting smaller, gradual failures. Key challenges identified include false positives, sensor malfunctions, and difficulties in integrating new models with older turbine systems. Advanced technologies such as digital twins, SCADA systems, and condition monitoring have significantly enhanced turbine maintenance practices. However, these technologies still require improvements, particularly in AI refinement and real-time data integration. The findings emphasize the need for continuous development to fully optimize wind turbine performance and support the broader adoption of renewable energy.
Dual-Objective Reinforcement Learning with Novel Hamilton-Jacobi-Bellman Formulations
Authors: William Sharpless, Dylan Hirsch, Sander Tonkens, Nikhil Shinde, Sylvia Herbert
Date: 2025-06-19 00:00:00
Tasks: Reinforcement Learning (RL)

Hard constraints in reinforcement learning (RL), whether imposed via the reward function or the model architecture, often degrade policy performance. Lagrangian methods offer a way to blend objectives with constraints, but often require intricate reward engineering and parameter tuning. In this work, we extend recent advances that connect Hamilton-Jacobi (HJ) equations with RL to propose two novel value functions for dual-objective satisfaction. Namely, we address: (1) the Reach-Always-Avoid problem - of achieving distinct reward and penalty thresholds - and (2) the Reach-Reach problem - of achieving thresholds of two distinct rewards. In contrast with temporal logic approaches, which typically involve representing an automaton, we derive explicit, tractable Bellman forms in this context by decomposing our problem into reach, avoid, and reach-avoid problems, as to leverage these aforementioned recent advances. From a mathematical perspective, the Reach-Always-Avoid and Reach-Reach problems are complementary and fundamentally different from standard sum-of-rewards problems and temporal logic problems, providing a new perspective on constrained decision-making. We leverage our analysis to propose a variation of Proximal Policy Optimization (DO-HJ-PPO), which solves these problems. Across a range of tasks for safe-arrival and multi-target achievement, we demonstrate that DO-HJ-PPO produces qualitatively distinct behaviors from previous approaches and out-competes a number of baselines in various metrics.
Pieceformer: Similarity-Driven Knowledge Transfer via Scalable Graph Transformer in VLSI
Authors: Hang Yang, Yusheng Hu, Yong liu, Cong, Hao
Date: 2025-06-18 00:00:00
Tasks: Graph Similarity, Management, Transfer Learning

Accurate graph similarity is critical for knowledge transfer in VLSI design, enabling the reuse of prior solutions to reduce engineering effort and turnaround time. We propose Pieceformer, a scalable, self-supervised similarity assessment framework, equipped with a hybrid message-passing and graph transformer encoder. To address transformer scalability, we incorporate a linear transformer backbone and introduce a partitioned training pipeline for efficient memory and parallelism management. Evaluations on synthetic and real-world CircuitNet datasets show that Pieceformer reduces mean absolute error (MAE) by 24.9% over the baseline and is the only method to correctly cluster all real-world design groups. We further demonstrate the practical usage of our model through a case study on a partitioning task, achieving up to 89% runtime reduction. These results validate the framework's effectiveness for scalable, unbiased design reuse in modern VLSI systems.
A Data-Integrated Framework for Learning Fractional-Order Nonlinear Dynamical Systems
Authors: Bahram Yaghooti, Chengyu Li, Bruno Sinopoli
Date: 2025-06-18 00:00:00

This paper presents a data-integrated framework for learning the dynamics of fractional-order nonlinear systems in both discrete-time and continuous-time settings. The proposed framework consists of two main steps. In the first step, input-output experiments are designed to generate the necessary datasets for learning the system dynamics, including the fractional order, the drift vector field, and the control vector field. In the second step, these datasets, along with the memory-dependent property of fractional-order systems, are used to estimate the system's fractional order. The drift and control vector fields are then reconstructed using orthonormal basis functions. To validate the proposed approach, the algorithm is applied to four benchmark fractional-order systems. The results confirm the effectiveness of the proposed framework in learning the system dynamics accurately. Finally, the same datasets are used to learn equivalent integer-order models. The numerical comparisons demonstrate that fractional-order models better capture long-range dependencies, highlighting the limitations of integer-order representations.
Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning
Authors: Ali Baheri
Date: 2025-06-14 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Cooperative multi-agent reinforcement learning (MARL) demands principled mechanisms to align heterogeneous policies while preserving the capacity for specialized behavior. We introduce a novel consensus framework that defines the team strategy as the entropic-regularized $p$-Wasserstein barycenter of agents' joint state--action visitation measures. By augmenting each agent's policy objective with a soft penalty proportional to its Sinkhorn divergence from this barycenter, the proposed approach encourages coherent group behavior without enforcing rigid parameter sharing. We derive an algorithm that alternates between Sinkhorn-barycenter computation and policy-gradient updates, and we prove that, under standard Lipschitz and compactness assumptions, the maximal pairwise policy discrepancy contracts at a geometric rate. Empirical evaluation on a cooperative navigation case study demonstrates that our OT-barycenter consensus outperforms an independent learners baseline in convergence speed and final coordination success.
DATA-DRIVEN PRONTO: a Model-free Solution for Numerical Optimal Control
Authors: Marco Borghesi, Lorenzo Sforni, Giuseppe Notarstefano
Date: 2025-06-18 00:00:00

This article addresses the problem of data-driven numerical optimal control for unknown nonlinear systems. In our scenario, we suppose to have the possibility of performing multiple experiments (or simulations) on the system. Experiments are performed by relying on a data-driven tracking controller able to steer the system towards a desired reference. Our proposed DATA-DRIVEN PRONTO algorithm iteratively refines a tentative solution of the optimal control problem by computing an approximate descent direction via a local trajectory perturbation. At each iteration, multiple trajectories are gathered by perturbing the current trajectory with a suitable dither signal, and then used to obtain a data-driven, time-varying linearization. The exploration is guided by the tracking controller, so that perturbed trajectories are obtained in closed loop. We show local convergence of DATA-DRIVEN PRONTO to a ball about an isolated optimal solution, whose radius depends on the amplitude of the dither signal. We corroborate the theoretical results by applying it to an underactuated robot.
Multi-dimensional evaluation on a rural integrated energy system including solar, wind, biomass and geothermal energy
Authors: Ruonan Lia, Chang Wena, Mingyu Yan, Congcong Wu, Ahmed Lotfy Elrefai, Xiaotong Zhang, Sahban Wael Saeed Alnaser
Date: 2025-06-18 00:00:00

This study focuses on the novel municipal-scale rural integrated energy system (RIES), which encompasses energy supply and application. By constructing a seven-dimensional evaluation system including energy efficiency, energy supply, low-carbon sustainability, environmental impact, energy economy, social benefits, and integrated energy system development, this research combines the improved analytic hierarchy process (IAHP) and entropy weight method (EWM) by sum of squares of deviations to balance expert experience and data objectivity. Furthermore, the cloud model is introduced to handle the fuzziness and randomness in the evaluation. This method can quantify the differences in system performance before and after the planning implementation. The results indicate that after planning, the comprehensive score has increased from 83.12 to 87.55, the entropy value has decreased from 6.931 to 5.336, indicating enhanced system stability. The hyper-entropy has dropped from 3.08 to 2.278, reflecting a reduction in uncertainty. The research findings provide a scientific basis for the planning optimization, policy-making, and sustainable development of rural integrated energy systems, possessing both theoretical innovation and practical guiding value.
Disruption of parkinsonian brain oscillations
Authors: Cédric Join, Jakub Orłowski, Antoine Chaillet, Madeleine Lowery, Hugues Mounier, Michel Fliess
Date: 2025-06-18 00:00:00

Deep brain stimulation (DBS) is an advanced surgical treatment for the symptoms of Parkinson's disease (PD), involving electrical stimulation of neurons within the basal ganglia region of the brain. DBS is traditionally delivered in an open-loop manner using fixed stimulation parameters, which may lead to suboptimal results. In an effort to overcome these limitations, closed loop DBS, using pathological subthalamic beta (13--30 Hz) activity as a feedback signal, offers the potential to adapt DBS automatically in response to changes in patient symptoms and side effects. However, clinically implemented closed-loop techniques have been limited to date to simple control algorithms, due to the inherent uncertainties in the dynamics involved. Model-free control, which has already seen successful applications in the field of bioengineering, offers a way to avoid this limitation and provides an alternative method to apply modern control approach to selective suppression of pathological oscillations.
Quantum Fisher-Preconditioned Reinforcement Learning: From Single-Qubit Control to Rayleigh-Fading Link Adaptation
Authors: Oluwaseyi Giwa, Muhammad Ahmed Mohsin, Muhammad Ali Jamshed
Date: 2025-06-18 00:00:00

In this letter, we propose Quantum-Preconditioned Policy Gradient (QPPG), a natural gradient-based algorithm for link adaptation that whitens policy updates using the full inverse quantum Fisher information with Tikhonov regularization. QPPG bridges classical and quantum geometry, achieving stable learning even under noise. Evaluated on classical and quantum environments, including noisy single-qubit Gym tasks and Rayleigh-fading channels, QPPG converges 4 times faster than REINFORCE and sustains a 1 dB gain under uncertainty. It reaches a 90 percent return in one hundred episodes with high noise robustness, showcasing the advantages of full QFI-based preconditioning for scalable quantum reinforcement learning.
Microgrid Operation Control with Adaptable Droop Gains
Authors: E. D. Gomez Anccas, C. A. Hans, D. Schulz
Date: 2025-06-18 00:00:00
Tasks: Model Predictive Control

Modern low-carbon power systems come with many challenges, such as increased inverter penetration and increased uncertainty from renewable sources and loads. In this context, the microgrid concept is a promising approach, which is based on a segmentation of the grid into independent smaller cells that can run either in grid-connected or standalone mode.In microgrids, droop control is widely used for primary control. It enables proportional power sharing, depending on the droop gains. Operation control schemes considering droop control often assume fixed droop gains. However, using adaptive droop gains for grid-forming units allow to shape power sharing in presence of fluctuations, enhancing flexibility while maintaining a safe microgrid operation, particularly under uncertainty. This work introduces a bilinear formulation for microgrid operation control that finds optimal power setpoints and droop gains on a timescale of minutes by solving a finite horizon optimization problem. In detail, a robust minmax model predictive control scheme is designed for a standalone microgrid, comprising a fuel cell, a photovoltaic system and an energy storage. Closed-loop simulations are performed with and without variable droop gains. The results show an increase in renewable utilization of up to 7.5 % while reducing the power output of the fuel cell by 6 %, when allowing variable droop gains.