pwc-archive/papers-with-abstracts huggingface dataset:
Enhanced Battery Capacity Estimation in Data-Limited Scenarios through Swarm Learning
Authors: Jiawei Zhang, Yu Zhang, Wei Xu, Yifei Zhang, Weiran Jiang, Qi Jiao, Yao Ren, Ziyou Song
Date: 2025-04-16 00:00:00
Tasks: Capacity Estimation, Management

Data-driven methods have shown potential in electric-vehicle battery management tasks such as capacity estimation, but their deployment is bottlenecked by poor performance in data-limited scenarios. Sharing battery data among algorithm developers can enable accurate and generalizable data-driven models. However, an effective battery management framework that simultaneously ensures data privacy and fault tolerance is still lacking. This paper proposes a swarm battery management system that unites a decentralized swarm learning (SL) framework and credibility weight-based model merging mechanism to enhance battery capacity estimation in data-limited scenarios while ensuring data privacy and security. The effectiveness of the SL framework is validated on a dataset comprising 66 commercial LiNiCoAlO2 cells cycled under various operating conditions. Specifically, the capacity estimation performance is validated in four cases, including data-balanced, volume-biased, feature-biased, and quality-biased scenarios. Our results show that SL can enhance the estimation accuracy in all data-limited cases and achieve a similar level of accuracy with central learning where large amounts of data are available.
SEROAISE: Advancing ROA Estimation for ReLU and PWA Dynamics through Estimating Certified Invariant Sets
Authors: Pouya Samanipour, Hasan Poonawala
Date: 2025-04-16 00:00:00

This paper presents a novel framework for constructing the Region of Attraction (RoA) for dynamics derived either from Piecewise Affine (PWA) functions or from Neural Networks (NNs) with Rectified Linear Units (ReLU) activation function. This method, described as Sequential Estimation of RoA based on Invariant Set Estimation (SEROAISE), computes a Lyapunov-like PWA function over a certified PWA invariant set. While traditional approaches search for Lyapunov functions by enforcing Lyapunov conditions over pre-selected domains, this framework enforces Lyapunov-like conditions over a certified invariant subset obtained using the Iterative Invariant Set Estimator(IISE). Compared to the state-of-the-art, IISE provides systematically larger certified invariant sets. In order to find a larger invariant subset, the IISE utilizes a novel concept known as the Non-Uniform Growth of Invariant Set (NUGIS). A number of examples illustrating the efficacy of the proposed methods are provided, including dynamical systems derived from learning algorithms. The implementation is publicly available at: https://github.com/PouyaSamanipour/SEROAISE.git.
Servo-Controllers with Operational Constraints
Authors: Eugene Lavretsky
Date: 2025-04-16 00:00:00

In this paper, a proportional-integral servo-control design method is developed for multi-input-multioutput linear time invariant systems with operational constraints imposed on the system control input and on an output of the same dimension as the control input. The design is based on min-norm controllers and Control Barrier Functions. It allows to enforce min/max box constraints by analytically solving Quadratic Programs for min-norm augmentation controllers. The method provides an anti-windup protection for the controller integrator state and enforces the desired operational control and output constraints, component-wise. A simulation example is given to illustrate potential benefits of the proposed design methodology for aerial flight critical systems.
Integrator Anti-Windup Design for Servo-Controllers with Position Constraints
Authors: Eugene Lavretsky
Date: 2025-04-16 00:00:00
Tasks: Position

A control design modification to prevent integrator windup for position saturated servo-controllers is introduced. The design is based on the formalism of Control Barrier Functions and represents an anti-windup integrator modification for position-limited servo-controllers. The method is applicable to Linear Time Invariant Multi-Input-Multi-Output open-loop stable continuous time systems. A flight control application example of the developed anti-windup control solution is discussed.
The CAM Model: An in vivo Testbed for Molecular Communication Systems
Authors: Fardad Vakilipoor, Andreas Ettner-Sitter, Lucas Brand, Sebastian Lotter, Thiha Aung, Silke Harteis, Robert Schober, Maximilian Schäfer
Date: 2025-04-16 00:00:00

Molecular communication (MC) research increasingly focuses on biomedical applications like health monitoring and drug delivery, demanding testing in realistic living environments. Elevating MC research requires developing advanced in vivo testbeds. We introduce the chorioallantoic membrane (CAM) model as the first versatile 3D in vivo MC platform. The CAM, a highly vascularized membrane in fertilized chicken eggs, is established in bioengineering, cancer research, and drug development. Its biological realism, reproducibility, and versatility make it ideal for next-generation MC testbeds, bridging proof-of-concept systems and practical applications. We comprehensively characterize the CAM model's properties and MC system relevance. Through experimental studies, we investigate fluorescent molecule distribution in the CAM's closed-loop vascular system. We derive an analytical model using the wrapped normal distribution to describe particle propagation in dispersive closed-loop systems dominated by diffusion and flow. Parametric models are developed to approximate particle dynamics in the CAM, with parameters estimated via nonlinear least squares curve fitting. A dataset of 69 regions from 25 eggs validates our models. We analyze parameter relationships and biological plausibility. Finally, we develop a parametric model for long-term particle behavior and liver accumulation in chick embryos.
Contract-based hierarchical control using predictive feasibility value functions
Authors: Felix Berkel, Kim Peter Wabersich, Hongxi Xiang, Elias Milios
Date: 2025-04-16 00:00:00
Tasks: Autonomous Driving, Computational Efficiency, Model Predictive Control

Today's control systems are often characterized by modularity and safety requirements to handle complexity, resulting in hierarchical control structures. Although hierarchical model predictive control offers favorable properties, achieving a provably safe, yet modular design remains a challenge. This paper introduces a contract-based hierarchical control strategy to improve the performance of control systems facing challenges related to model inconsistency and independent controller design across hierarchies. We consider a setup where a higher-level controller generates references that affect the constraints of a lower-level controller, which is based on a soft-constrained MPC formulation. The optimal slack variables serve as the basis for a contract that allows the higher-level controller to assess the feasibility of the reference trajectory without exact knowledge of the model, constraints, and cost of the lower-level controller. To ensure computational efficiency while maintaining model confidentiality, we propose using an explicit function approximation, such as a neural network, to represent the cost of optimal slack values. The approach is tested for a hierarchical control setup consisting of a planner and a motion controller as commonly found in autonomous driving.
Power Line Communication vs. Talkative Power Conversion: A Benchmarking Study
Authors: Peter A. Hoeher, Yang Leng, Maximilian Mewis, Rongwu Zhu
Date: 2025-04-16 00:00:00
Tasks: Benchmarking

The convergence of energy transmission and data communication has become a key feature of decentralized energy systems across a broad spectrum of voltage/power ranges, including smart grid applications and cyber-physical power systems. This paper compares two distinct approaches: Power Line Communications (PLC) and Talkative Power Conversion (TPC). While PLC leverages existing power infrastructure for data transmission by using external data transmitters and receivers, TPC integrates communication capabilities directly into power electronic converters. We present their technical foundations and applications, benchmark their strengths and bottlenecks, and outline future research directions regarding TPC that could bridge the gap between power and communication technologies.
Efficient identification of linear, parameter-varying, and nonlinear systems with noise models
Authors: Alberto Bemporad, Roland Tóth
Date: 2025-04-16 00:00:00
Tasks: Computational Efficiency

We present a general system identification procedure capable of estimating of a broad spectrum of state-space dynamical models, including linear time-invariant (LTI), linear parameter-varying} (LPV), and nonlinear (NL) dynamics, along with rather general classes of noise models. Similar to the LTI case, we show that for this general class of model structures, including the NL case, the model dynamics can be separated into a deterministic process and a stochastic noise part, allowing to seamlessly tune the complexity of the combined model both in terms of nonlinearity and noise modeling. We parameterize the involved nonlinear functional relations by means of artificial neural-networks (ANNs), although alternative parametric nonlinear mappings can also be used. To estimate the resulting model structures, we optimize a prediction-error-based criterion using an efficient combination of a constrained quasi-Newton approach and automatic differentiation, achieving training times in the order of seconds compared to existing state-of-the-art ANN methods which may require hours for models of similar complexity. We formally establish the consistency guarantees for the proposed approach and demonstrate its superior estimation accuracy and computational efficiency on several benchmark LTI, LPV, and NL system identification problems.
A Koopman Operator Approach to Data-Driven Control of Semilinear Parabolic Systems
Authors: Joachim Deutscher, Tarik Enderes
Date: 2025-04-16 00:00:00

This paper is concerned with the data-driven stabilization of unknown boundary controlled semilinear parabolic systems. The nonlinear dynamics of the system are lifted using a finite number of eigenfunctionals of the Koopman operator related to the autonomous semilinear PDE. This results in a novel data-driven finite-dimensional model of the lifted dynamics, which is amenable to apply design procedures for finite-dimensional systems to stabilize the semilinear parabolic system. In order to facilitate this, a bilinearization of the lifted dynamics is considered and feedback linearization is applied for the data-driven stabilization of the semilinear parabolic PDE. This reveals a novel connection between the assignment of eigenfunctionals to the closed-loop Koopman operator and feedback linearization. By making use of a modal representation, exponential stability of the closed-loop system in the presence of errors resulting from the data-driven computation of eigenfunctionals and the bilinearization is verified. The data-driven controller directly follows from applying generalized eDMD to state data available for the semilinear parabolic PDE. An example of an unstable semilinear reaction-diffusion system with finite-time blow up demonstrates the novel data-driven stabilization approach.
A Novel Splitter Design for RSMA Networks
Authors: Sawaira Rafaqat Ali, Shaima Abidrabbu, H. M. Furqan, Hüseyin Arslan
Date: 2025-04-16 00:00:00
Tasks: Management

Rate splitting multiple access (RSMA) has firmly established itself as a powerful methodology for multiple access, interference management, and multi-user strategy for next-generation communication systems. In this paper, we propose a novel channel-dependent splitter design for multi-carrier RSMA systems, aimed at improving reliability performance. Specifically, the proposed splitter leverages channel state information and the inherent structure of RSMA to intelligently replicate segments of the private stream data that are likely to encounter deep-faded subchannels into the common stream. Thus, the reliability is enhanced within the same transmission slot, minimizing the need for frequent retransmissions and thereby reducing latency. To assess the effectiveness of our approach, we conduct comprehensive evaluations using key performance metrics, including achievable sum rate, average packet delay, and bit error rate (BER), under both perfect and imperfect channel estimation scenarios.
A Novel Approach to Secure RSMA Networks
Authors: Shaima Abidrabbu, Hüseyin Arslan
Date: 2025-04-16 00:00:00

This letter introduces a novel data-dependent interleaving technique designed to enhance the security of rate-splitting multiple access (RSMA) networks by protecting the common stream from eavesdropping threats. Specifically, we exploit the RSMA structure by interleaving the common bits of each user based on a sequence derived from their private bits. By decoding its private stream, the legitimate receiver reconstructs the interleaving sequence set by the transmitter and successfully de-interleaves the common stream. Therefore, the common part is successfully prevented from being intercepted by an eavesdropper who is unable to deduce the dynamic changing interleaving permutations. To ensure dynamic interleaving sequences, a private bit selection approach that balances the trade-off between security and system efficiency is proposed. Simulation findings confirm the effectiveness of the suggested method, showing notable security improvements while maintaining robust overall system reliability.
Transferable Deployment of Semantic Edge Inference Systems via Unsupervised Domain Adaption
Authors: Weiqiang Jiao, Suzhi Bi, Xian Li, Cheng Guo, Hao Chen, Zhi Quan
Date: 2025-04-16 00:00:00
Tasks: Decoder, Domain Adaptation, Knowledge Distillation, Unsupervised Domain Adaptation

This paper investigates deploying semantic edge inference systems for performing a common image clarification task. In particular, each system consists of multiple Internet of Things (IoT) devices that first locally encode the sensing data into semantic features and then transmit them to an edge server for subsequent data fusion and task inference. The inference accuracy is determined by efficient training of the feature encoder/decoder using labeled data samples. Due to the difference in sensing data and communication channel distributions, deploying the system in a new environment may induce high costs in annotating data labels and re-training the encoder/decoder models. To achieve cost-effective transferable system deployment, we propose an efficient Domain Adaptation method for Semantic Edge INference systems (DASEIN) that can maintain high inference accuracy in a new environment without the need for labeled samples. Specifically, DASEIN exploits the task-relevant data correlation between different deployment scenarios by leveraging the techniques of unsupervised domain adaptation and knowledge distillation. It devises an efficient two-step adaptation procedure that sequentially aligns the data distributions and adapts to the channel variations. Numerical results show that, under a substantial change in sensing data distributions, the proposed DASEIN outperforms the best-performing benchmark method by 7.09% and 21.33% in inference accuracy when the new environment has similar or 25 dB lower channel signal to noise power ratios (SNRs), respectively. This verifies the effectiveness of the proposed method in adapting both data and channel distributions in practical transfer deployment applications.
Manifold meta-learning for reduced-complexity neural system identification
Authors: Marco Forgione, Ankush Chakrabarty, Dario Piga, Matteo Rufolo, Alberto Bemporad
Date: 2025-04-16 00:00:00
Tasks: Meta-Learning

System identification has greatly benefited from deep learning techniques, particularly for modeling complex, nonlinear dynamical systems with partially unknown physics where traditional approaches may not be feasible. However, deep learning models often require large datasets and significant computational resources at training and inference due to their high-dimensional parameterizations. To address this challenge, we propose a meta-learning framework that discovers a low-dimensional manifold within the parameter space of an over-parameterized neural network architecture. This manifold is learned from a meta-dataset of input-output sequences generated by a class of related dynamical systems, enabling efficient model training while preserving the network's expressive power for the considered system class. Unlike bilevel meta-learning approaches, our method employs an auxiliary neural network to map datasets directly onto the learned manifold, eliminating the need for costly second-order gradient computations during meta-training and reducing the number of first-order updates required in inference, which could be expensive for large models. We validate our approach on a family of Bouc-Wen oscillators, which is a well-studied nonlinear system identification benchmark. We demonstrate that we are able to learn accurate models even in small-data scenarios.
Analysis of Power Swing Characteristics of Grid-Forming VSC System Considering the Current Limitation Mode
Authors: Yongxin Xiong, Heng Wu, Yifei Li, Xiongfei Wang
Date: 2025-04-16 00:00:00

This paper investigates power swing characteristics of grid-forming voltage source converter (GFM-VSC) systems considering the current limitation mode in both non-inertial and inertial GFM-VSC systems. Following grid faults, non-inertial GFM-VSC systems can re-synchronize with the grid but may experience significant power swings driven by its control dynamics, while inertial GFM-VSC systems may exhibit loss of synchronization (LOS), characterized by the divergence of the output angle in the active power control loop. These behaviours are different from conventional synchronous generator (SG)-based systems, where power swings are typically characterized by physical angle deviations among power sources. Based on these findings, this paper explores the performance of traditional impedance-based swing detection schemes in GFM-VSC systems. The theoretical analysis is validated through various simulations using the PSCAD/EMTDC platform, covering both single and multi-machine system scenarios.
Optimal SVI-Weighted PSPS Decisions with Decision-Dependent Outage Uncertainty
Authors: Ryan Greenough, Kohei Murakami, Jan Kleissl, Adil Khurram
Date: 2025-04-15 00:00:00

Public Safety Power Shutoffs (PSPS) are a pre-emptive strategy to mitigate the wildfires caused by power system malfunction. System operators implement PSPS to balance wildfire mitigation efforts through de-energization of transmission lines against the risk of widespread blackouts modeled with load shedding. Existing approaches do not incorporate decision-dependent wildfire-driven failure probabilities, as modeling outage scenario probabilities requires incorporating high-order polynomial terms in the objective. This paper uses distribution shaping to develop an efficient MILP problem representation of the distributionally robust PSPS problem. Building upon the author's prior work, the wildfire risk of operating a transmission line is a function of the probability of a wildfire-driven outage and its subsequent expected impact in acres burned. A day-ahead unit commitment and line de-energization PSPS framework is used to assess the trade-off between total cost and wildfire risk at different levels of distributional robustness, parameterized by a level of distributional dissimilarity $\kappa$. We perform simulations on the IEEE RTS 24-bus test system.
Reachability Analysis of Nonlinear Discrete-Time Systems Using Polyhedral Relaxations and Constrained Zonotopes
Authors: Brenner S. Rego, Guilherme V. Raffo, Marco H. Terra, Joseph K. Scott
Date: 2025-04-15 00:00:00

This paper presents a novel algorithm for reachability analysis of nonlinear discrete-time systems. The proposed method combines constrained zonotopes (CZs) with polyhedral relaxations of factorable representations of nonlinear functions to propagate CZs through nonlinear functions, which is normally done using conservative linearization techniques. The new propagation method provides better approximations than those resulting from linearization procedures, leading to significant improvements in the computation of reachable sets in comparison to other CZ methods from the literature. Numerical examples highlight the advantages of the proposed algorithm.
Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling
Authors: Feng Zhu, Aritra Mitra, Robert W. Heath
Date: 2025-04-15 00:00:00
Tasks: Reinforcement Learning (RL)

Motivated by collaborative reinforcement learning (RL) and optimization with time-correlated data, we study a generic federated stochastic approximation problem involving $M$ agents, where each agent is characterized by an agent-specific (potentially nonlinear) local operator. The goal is for the agents to communicate intermittently via a server to find the root of the average of the agents' local operators. The generality of our setting stems from allowing for (i) Markovian data at each agent and (ii) heterogeneity in the roots of the agents' local operators. The limited recent work that has accounted for both these features in a federated setting fails to guarantee convergence to the desired point or to show any benefit of collaboration; furthermore, they rely on projection steps in their algorithms to guarantee bounded iterates. Our work overcomes each of these limitations. We develop a novel algorithm titled \texttt{FedHSA}, and prove that it guarantees convergence to the correct point, while enjoying an $M$-fold linear speedup in sample-complexity due to collaboration. To our knowledge, \emph{this is the first finite-time result of its kind}, and establishing it (without relying on a projection step) entails a fairly intricate argument that accounts for the interplay between complex temporal correlations due to Markovian sampling, multiple local steps to save communication, and the drift-effects induced by heterogeneous local operators. Our results have implications for a broad class of heterogeneous federated RL problems (e.g., policy evaluation and control) with function approximation, where the agents' Markov decision processes can differ in their probability transition kernels and reward functions.
Verifiable Mission Planning For Space Operations
Authors: Quentin Rommel, Michael Hibbard, Pavan Shukla, Himanshu Save, Srinivas Bettadpur, Ufuk Topcu
Date: 2025-04-15 00:00:00

As space missions become more complex, planning methods must maximize mission performance while rigorously enforcing safety. We develop a probabilistic approach based on a finite-horizon Markov decision process to optimize spacecraft operations planning with safety guarantees. In the model, states capture essential mission parameters, and actions represent the operational adjustments needed to meet mission objectives. By directly incorporating uncertainties from environmental conditions and spacecraft dynamics, an optimal sequence of actions is computed that maximizes expected rewards and strictly enforces safety constraints. Numerical experiments on the GRACE-FO mission demonstrate robust performance under uncertainties while providing probabilistic safety guarantees, offering a reliable solution for autonomous spacecraft operations.