pwc-archive/papers-with-abstracts huggingface dataset:
A Fully Self-Synchronized Control for Hybrid Series-Parallel Electronized Power Networks
Authors: Zexiong Wei, Yao Sun, Xiaochao Hou, Mei Su
Date: 2025-03-02 00:00:00

The hybrid series-parallel system is the final form of the power electronics-enabled power system, which combines the advantages of both series and parallel connections. Although self-synchronization of parallel-type and series-type systems is well known, self-synchronization of hybrid systems remains unrevealed. To fill in this gap, a fully self-synchronized control for hybrid series-parallel system is proposed in this paper. Based on the self-synchronization mechanism of power angle in parallel-type system and power factor angle in series-type system, a decentralized control strategy by integration of power droop and power factor angle droop can realize self-synchronization and power balancing of each module in the hybrid system.
Extended Impedance Modal Analysis of Internal Dynamics in Grid-Following Inverters
Authors: Le Zheng, Jiajie Zheng, Lei Chen, Chongru Liu
Date: 2025-03-02 00:00:00

As the penetration of Grid-Following inverters (GFL) in power systems continues to increase, the dynamic characteristics of power systems undergo significant transformations. Recently, modal analysis based on the impedance model (MAI) has been utilized to evaluate the interaction between GFLs and the power grid through impedance/admittance participation factors (PF). However, MAI relies on the impedance model that characterizes the input-output behavior of the ports, treating the GFL as a single integrated entity. This approach limits its ability to reveal the complex dynamic coupling mechanism between different control loops inside the GFL. In this paper, we propose an extended impedance modal analysis (EMAI) method. Firstly, the equivalent dynamics of the GFL are decomposed into two components: the synchronous dynamic dominated by the phase-locked loop (PLL) and the electromagnetic dynamic dominated by the current control loop (CCL). The PF and participation ratios (PR) of these two dynamic components are then calculated to identify the dominant dynamics of the system. Building on this, we introduce the explicit parameter participation factors (PPF) to further pinpoint the key parameters of the dominant control loop, which provides a way for enhancing system stability. Finally, the effectiveness of the proposed method is validated through the simulation of the modified 14-bus and 68-bus systems. The EMAI method enables the analysis of the dynamic characteristics of each control loop in the GFL based on the impedance model. It can effectively identify the critical control loops influencing system stability without requiring the construction of a full state-space model, demonstrating its broad applicability and value.
Learning for Feasible Region on Coal Mine Virtual Power Plants with Imperfect Information
Authors: Hongxu Huang, Ruike Lyu, Cheng Feng, Haiwang Zhong, H. B. Gooi, Bo Li, Rui Liang
Date: 2025-03-02 00:00:00

The feasible region assessment (FRA) in industrial virtual power plants (VPPs) is driven by the need to activate large-scale latent industrial loads for demand response, making it essential to aggregate these flexible resources for peak regulation. However, the large number of devices and the need for privacy preservation in coal mines pose challenges to accurately aggregating these resources into a cohesive coal mine VPP. In this paper, we propose an efficient and reliable data-driven approach for FRA in the coal mine VPP that can manage incomplete information. Our data-driven FRA algorithm approximates equipment and FRA parameters based on historical energy dispatch data, effectively addressing the challenges of imperfect information. Simulation results illustrate that our method approximates the accurate feasible operational boundaries under dynamic and imperfect information conditions.
Zero-Power Backscatter Sensing and Communication Proof-of-Concept
Authors: Yu Zhang, Xiaoyu Shi, Tongyang Xu
Date: 2025-03-01 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we present an experimental setup to evaluate the performance of a radio frequency identification (RFID)-based integrated sensing and communication (ISAC) system. We focus on both the communication and sensing capabilities of the system. Our experiments evaluate the system's performance in various channel fading scenarios and with different substrate materials, including wood, plastic, wall, and glass. Additionally, we utilize radio tomographic imaging (RTI) to detect human motion by analyzing received signal strength indicator (RSSI) data. Our results demonstrate the impact of different materials and environments on RSSI and highlight the potential of RFID-based systems for effective sensing and communication in diverse applications.
Co-state Neural Network for Real-time Nonlinear Optimal Control with Input Constraints
Authors: Lihan Lian, Uduak Inyang-Udoh
Date: 2025-03-01 00:00:00

In this paper, we propose a method to solve nonlinear optimal control problems (OCPs) with constrained control input in real-time using neural networks (NNs). We introduce what we have termed co-state Neural Network (CoNN) that learns the mapping from any given state value to its corresponding optimal co-state trajectory based on the Pontryagin's Minimum (Maximum) Principle (PMP). In essence, the CoNN parameterizes the Two-Point Boundary Value Problem (TPBVP) that results from the PMP for various initial states. The CoNN is trained using data generated from numerical solutions of TPBVPs for unconstrained OCPs to learn the mapping from a state to its corresponding optimal co-state trajectory. For better generalizability, the CoNN is also trained to respect the first-order optimality conditions (system dynamics). The control input constraints are satisfied by solving a quadratic program (QP) given the predicted optimal co-states. We demonstrate the effectiveness of our CoNN-based controller in a feedback scheme for numerical examples with both unconstrained and constrained control input. We also verify that the controller can handle unknown disturbances effectively.
Model-Agnostic Meta-Policy Optimization via Zeroth-Order Estimation: A Linear Quadratic Regulator Perspective
Authors: Yunian Pan, Tao Li, Quanyan Zhu
Date: 2025-03-01 00:00:00
Tasks: image-classification, Image Classification, Meta-Learning

Meta-learning has been proposed as a promising machine learning topic in recent years, with important applications to image classification, robotics, computer games, and control systems. In this paper, we study the problem of using meta-learning to deal with uncertainty and heterogeneity in ergodic linear quadratic regulators. We integrate the zeroth-order optimization technique with a typical meta-learning method, proposing an algorithm that omits the estimation of policy Hessian, which applies to tasks of learning a set of heterogeneous but similar linear dynamic systems. The induced meta-objective function inherits important properties of the original cost function when the set of linear dynamic systems are meta-learnable, allowing the algorithm to optimize over a learnable landscape without projection onto the feasible set. We provide stability and convergence guarantees for the exact gradient descent process by analyzing the boundedness and local smoothness of the gradient for the meta-objective, which justify the proposed algorithm with gradient estimation error being small. We provide the sample complexity conditions for these theoretical guarantees, as well as a numerical example at the end to corroborate this perspective.
Adaptive Wall-Following Control for Unmanned Ground Vehicles Using Spiking Neural Networks
Authors: Hengye Yang, Yanxiao Chen, Zexuan Fan, Lin Shao, Tao Sun
Date: 2025-03-01 00:00:00
Tasks: State Estimation

Unmanned ground vehicles operating in complex environments must adaptively adjust to modeling uncertainties and external disturbances to perform tasks such as wall following and obstacle avoidance. This paper introduces an adaptive control approach based on spiking neural networks for wall fitting and tracking, which learns and adapts to unforeseen disturbances. We propose real-time wall-fitting algorithms to model unknown wall shapes and generate corresponding trajectories for the vehicle to follow. A discretized linear quadratic regulator is developed to provide a baseline control signal based on an ideal vehicle model. Point matching algorithms then identify the nearest matching point on the trajectory to generate feedforward control inputs. Finally, an adaptive spiking neural network controller, which adjusts its connection weights online based on error signals, is integrated with the aforementioned control algorithms. Numerical simulations demonstrate that this adaptive control framework outperforms the traditional linear quadratic regulator in tracking complex trajectories and following irregular walls, even in the presence of partial actuator failures and state estimation errors.
Efficient Fault Diagnosis in Lithium-Ion Battery Packs: A Structural Approach with Moving Horizon Estimation
Authors: Amir Farakhor, Di wu, Yebin Wang, Huazhen Fang
Date: 2025-03-01 00:00:00
Tasks: Fault Diagnosis

Safe and reliable operation of lithium-ion battery packs depends on effective fault diagnosis. However, model-based approaches often encounter two major challenges: high computational complexity and extensive sensor requirements. To address these bottlenecks, this paper introduces a novel approach that harnesses the structural properties of battery packs, including cell uniformity and the sparsity of fault occurrences. We integrate this approach into a Moving Horizon Estimation (MHE) framework and estimate fault signals such as internal and external short circuits and faults in voltage and current sensors. To mitigate computational demands, we propose a hierarchical solution to the MHE problem. The proposed solution breaks up the pack-level MHE problem into smaller problems and solves them efficiently. Finally, we perform extensive simulations across various battery pack configurations and fault types to demonstrate the effectiveness of the proposed approach. The results highlight that the proposed approach simultaneously reduces the computational demands and sensor requirements of fault diagnosis.
Traffic Priority-Aware 5G NR-U/Wi-Fi Coexistence with Deep Reinforcement Learning
Authors: Mohammad Reza Fasihi, Brian L. Mark
Date: 2025-03-01 00:00:00
Tasks: Deep Reinforcement Learning, Fairness

Coexistence of 5G new radio unlicensed (NR-U) and Wi-Fi is highly prone to the collisions among NR-U gNBs (5G base stations) and Wi-Fi APs (access points). To improve performance and fairness for both networks, various collision resolution mechanisms have been proposed to replace the simple listen-before-talk (LBT) scheme used in the current 5G standard. We address two gaps in the literature: first, the lack of a comprehensive performance comparison among the proposed collision resolution mechanisms and second, the impact of multiple traffic priority classes. Through extensive simulations, we compare the performance of several recently proposed collision resolution mechanisms for NR-U/Wi-Fi coexistence. We extend one of these mechanisms to handle multiple traffic priorities. We then develop a traffic-aware multi-objective deep reinforcement learning algorithm for the scenario of coexistence of high-priority traffic gNB user equipment (UE) with multiple lower-priority traffic UEs and Wi-Fi stations. The objective is to ensure low latency for high-priority gNB traffic while increasing the airtime fairness among the NR-U and Wi-Fi networks. Our simulation results show that the proposed algorithm lowers the channel access delay of high-priority traffic while improving the fairness among both networks.
Adaptive Reinforcement Learning for State Avoidance in Discrete Event Systems
Authors: Md Nur-A-Adam Dony
Date: 2025-02-28 00:00:00
Tasks: Decision Making, Reinforcement Learning (RL)

Reinforcement learning (RL) has emerged as a potent paradigm for autonomous decision-making in complex environments. However, the integration of event-driven decision processes within RL remains a challenge. This paper presents a novel architecture that combines a Discrete Event Supervisory (DES) model with a standard RL framework to create a hybrid decision-making system. Our model leverages the DES's capabilities in managing event-based dynamics with the RL agent's adaptability to continuous states and actions, facilitating a more robust and flexible control strategy in systems characterized by both continuous and discrete events. The DES model operates alongside the RL agent, enhancing the policy's performance with event-based insights, while the environment's state transitions are governed by a mechanistic model. We demonstrate the efficacy of our approach through simulations that show improved performance metrics over traditional RL implementations. Our results suggest that this integrated approach holds promise for applications ranging from industrial automation to intelligent traffic systems, where discrete event handling is paramount.
Tracking Control of Euler-Lagrangian Systems with Prescribed State, Input, and Temporal Constraints
Authors: Chidre Shravista Kashyap, Pushpak Jagtap, Jishnu Keshavan
Date: 2025-02-25 00:00:00

The synthesis of a smooth tracking control policy for Euler-Lagrangian (EL) systems with stringent regions of operation induced by state, input and temporal (SIT) constraints is a very challenging task. In contrast with existing methods that utilize prior knowledge of EL model parameters and uncertainty bounds, this study proposes an approximation-free adaptive barrier function-based control policy to ensure local prescribed time convergence of tracking error under state and input constraints. The proposed control policy accomplishes this by utilizing smooth time-based generator functions embedded in the filtered tracking error, which is combined with a saturation function that limits control action and confines states within the prescribed limits by enforcing the time-varying bounds on the filtered tracking error. Importantly, corresponding feasibility conditions pertaining to the minimum control authority, maximum disturbance rejection capability of the control policy, and the viable set of initial conditions are derived, illuminating the narrow operating domain of the EL systems arising from the interplay of SIT constraints. Numerical validation studies with three different robotic manipulators are employed to demonstrate the efficacy of the proposed scheme. A detailed performance comparison study with leading alternative designs is also undertaken to illustrate the superior performance of the proposed scheme.
Clearing function-based release date optimization in a multi-item multi-stage MRP planned production system in a rolling-horizon planning environment with multilevel BOM
Authors: Wolfgang Seiringer, Klaus Altendorfer, Reha Uzsoy
Date: 2025-02-24 00:00:00
Tasks: Scheduling

This study explores the integration of clearing function (CF)-based release planning into Material Requirements Planning (MRP) systems, with a focus on mitigating the inherent rigidity of MRP in handling variability in production environments. By replacing the backward scheduling step of MRP with a CF-based optimization model, this work investigates its impact on overall costs, lead times, and system performance in multi-item, multi-stage production systems. Computational experiments were conducted on two distinct systems - a simple and a complex production system - under varying demand behaviors and utilization levels. The findings reveal that the CF-based release order planning consistently outperforms traditional MRP in managing cost and variability, particularly under scenarios of higher demand uncertainty and shop load. While MRP demonstrates stability in less complex scenarios, its inability to adapt dynamically to variability leads to higher costs in most conditions. The analysis underscores the potential of CF-based methods to enhance planning efficiency in dynamic production settings, providing a foundation for future research on integrating capacity constraints within the broader MRP framework.
A Review of Artificial Intelligence Impacting Statistical Process Monitoring and Future Directions
Authors: Shing I Chang, Parviz Ghafariasl
Date: 2025-02-23 00:00:00

It has been 100 years since statistical process control (SPC) or statistical process monitoring (SPM) was first introduced for production processes and later applied to service, healthcare, and other industries. The techniques applied to SPM applications are mostly statistically oriented. Recent advances in Artificial Intelligence (AI) have reinvigorated the imagination of adopting AI for SPM applications. This manuscript begins with a concise review of the historical development of the statistically based SPM methods. Next, this manuscript explores AI and Machine Learning (ML) algorithms and methods applied in various SPM applications, addressing quality characteristics of univariate, multivariate, profile, and image. These AI methods can be classified into the following categories: classification, pattern recognition, time series applications, and generative AI. Specifically, different kinds of neural networks, such as artificial neural networks (ANN), convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial networks (GAN), are among the most implemented AI methods impacting SPM. Finally, this manuscript outlines a couple of future directions that harness the potential of the Large Multimodal Model (LMM) for advancing SPM research and applications in complex systems. The ultimate objective is to transform statistical process monitoring (SPM) into smart process control (SMPC), where corrective actions are autonomously implemented to either prevent quality issues or restore process performance.
A strictly predefined-time convergent and anti-noise fractional-order zeroing neural network for solving time-variant quadratic programming in kinematic robot control
Authors: Yi Yang, Xiao Li, Xuchen Wang, Mei Liu, Junwei Yin, Weibing Li, Richard M. Voyles, Xin Ma
Date: 2025-02-22 00:00:00
Tasks: Computational Efficiency

This paper proposes a strictly predefined-time convergent and anti-noise fractional-order zeroing neural network (SPTC-AN-FOZNN) model, meticulously designed for addressing time-variant quadratic programming (TVQP) problems. This model marks the first variable-gain ZNN to collectively manifest strictly predefined-time convergence and noise resilience, specifically tailored for kinematic motion control of robots. The SPTC-AN-FOZNN advances traditional ZNNs by incorporating a conformable fractional derivative in accordance with the Leibniz rule, a compliance not commonly achieved by other fractional derivative definitions. It also features a novel activation function designed to ensure favorable convergence independent of the model's order. When compared to five recently published recurrent neural networks (RNNs), the SPTC-AN-FOZNN, configured with $0<\alpha\leq 1$, exhibits superior positional accuracy and robustness against additive noises for TVQP applications. Extensive empirical evaluations, including simulations with two types of robotic manipulators and experiments with a Flexiv Rizon robot, have validated the SPTC-AN-FOZNN's effectiveness in precise tracking and computational efficiency, establishing its utility for robust kinematic control.
Interaction-Aware Model Predictive Decision-Making for Socially-Compliant Autonomous Driving in Mixed Urban Traffic Scenarios
Authors: Balint Varga, Thomas Brand, Marcus Schmitz, Ehsan Hashemi
Date: 2025-02-21 00:00:00
Tasks: Autonomous Driving, Decision Making

This paper presents the experimental validation of an interaction-aware model predictive decision-making (IAMPDM) approach in the course of a simulator study. The proposed IAMPDM uses a model of the pedestrian, which simultaneously predicts their future trajectories and characterizes the interaction between the pedestrian and the automated vehicle. The main benefit of the proposed concept and the experiment is that the interaction between the pedestrian and the socially compliant autonomous vehicle leads to smoother traffic. Furthermore, the experiment features a novel human-in-the-decision-loop aspect, meaning that the test subjects have no expected behavior or defined sequence of their actions, better imitating real traffic scenarios. Results show that intention-aware decision-making algorithms are more effective in realistic conditions and contribute to smoother traffic flow than state-of-the-art solutions. Furthermore, the findings emphasize the crucial impact of intention-aware decision-making on autonomous vehicle performance in urban areas and the need for further research.
A New $\sim 5σ$ Tension at Characteristic Redshift from DESI-DR1 BAO and DES-SN5YR Observations
Authors: Purba Mukherjee, Anjan A Sen
Date: 2025-03-04 00:00:00

We perform a model-independent reconstruction of the angular diameter distance ($D_{A}$) using the Multi-Task Gaussian Process (MTGP) framework with DESI-DR1 BAO and DES-SN5YR datasets. We calibrate the comoving sound horizon at the baryon drag epoch $r_d$ to the Planck best-fit value, ensuring consistency with early-universe physics. With the reconstructed $D_A$ at two key redshifts, $z\sim 1.63$ (where $D_{A}^{\prime} =0$) and at $z\sim 0.512$ (where $D_{A}^{\prime} = D_{A}$), we derive the expansion rate of the Universe $H(z)$ at these redshifts. Our findings reveal that at $z\sim 1.63$, the $H(z)$ is fully consistent with the Planck-2018 $\Lambda$CDM prediction, confirming no new physics at that redshift. However, at $z \sim 0.512$, the derived $H(z)$ shows a more than $5\sigma$ discrepancy with the Planck-2018 $\Lambda$CDM prediction, suggesting a possible breakdown of the $\Lambda$CDM model as constrained by Planck-2018 at this lower redshift. This emerging $\sim 5\sigma$ tension at $z\sim 0.512$, distinct from the existing ``Hubble Tension'', may signal the first strong evidence for new physics at low redshifts.
Towards a robust R2D2 paradigm for radio-interferometric imaging: revisiting DNN training and architecture
Authors: Amir Aghabiglou, Chung San Chu, Chao Tang, Arwa Dabbech, Yves Wiaux
Date: 2025-03-04 00:00:00
Tasks: Image Reconstruction, Radio Interferometry

The R2D2 Deep Neural Network (DNN) series was recently introduced for image formation in radio interferometry. It can be understood as a learned version of CLEAN, whose minor cycles are substituted with DNNs. We revisit R2D2 on the grounds of series convergence, training methodology, and DNN architecture, improving its robustness in terms of generalisability beyond training conditions, capability to deliver high data fidelity, and epistemic uncertainty. Firstly, while still focusing on telescope-specific training, we enhance the learning process by randomising Fourier sampling integration times, incorporating multi-scan multi-noise configurations, and varying imaging settings, including pixel resolution and visibility-weighting scheme. Secondly, we introduce a convergence criterion whereby the reconstruction process stops when the data residual is compatible with noise, rather than simply using all available DNNs. This not only increases the reconstruction efficiency by reducing its computational cost, but also refines training by pruning out the data/image pairs for which optimal data fidelity is reached before training the next DNN. Thirdly, we substitute R2D2's early U-Net DNN with a novel architecture (U-WDSR) combining U-Net and WDSR, which leverages wide activation, dense connections, weight normalisation, and low-rank convolution to improve feature reuse and reconstruction precision. As previously, R2D2 was trained for monochromatic intensity imaging with the Very Large Array (VLA) at fixed $512 \times 512$ image size. Simulations on a wide range of inverse problems and a case study on real data reveal that the new R2D2 model consistently outperforms its earlier version in image reconstruction quality, data fidelity, and epistemic uncertainty.