pwc-archive/papers-with-abstracts huggingface dataset:
Three-Dimensional Nonlinear Guidance with Impact Time and Field-of-view Constraints
Authors: Ashok R Samrat, Swati Singh, Shashi Ranjan Kumar
Date: 2025-05-24 00:00:00

This paper addresses the time-constrained interception of targets at a predetermined time with bounded field-of-view capability of the seeker-equipped interceptors. We propose guidance laws using the effective lead angle and velocity lead angles of the interceptor to achieve a successful interception of the target. The former scheme extends the existing two-dimensional guidance strategy to a three-dimensional setting. We have shown that such an extension may result in high-frequency switching in the input demand, which may degrade the interceptor's performance. To overcome the potential limitations of such a guidance strategy, we propose an elegant solution using the velocity lead angles and the range error with a backstepping technique. Using the velocity lead angles as virtual inputs, the effective lead angle profile is subsequently regulated to satisfy the seeker's field-of-view bound. Unlike the existing strategies, the proposed guidance strategy does not rely on the time-to-go estimate, which is an appealing feature of the design, as the time-to-go estimate may not always be available with high precision. We provide a theoretical analysis of the error variable and subsequently analytically derive the bounds on achievable impact times. Numerical simulations are performed to support the theoretical findings. The performance of the proposed guidance strategy is compared with that of an existing one, and it has been shown to yield better performance. Finally, a study on different choices of virtual inputs is also provided.
Agent-Based Decentralized Energy Management of EV Charging Station with Solar Photovoltaics via Multi-Agent Reinforcement Learning
Authors: Jiarong Fan, Chenghao Huang, Hao Wang
Date: 2025-05-24 00:00:00
Tasks: energy management, Management, Multi-agent Reinforcement Learning

In the pursuit of energy net zero within smart cities, transportation electrification plays a pivotal role. The adoption of Electric Vehicles (EVs) keeps increasing, making energy management of EV charging stations critically important. While previous studies have managed to reduce energy cost of EV charging while maintaining grid stability, they often overlook the robustness of EV charging management against uncertainties of various forms, such as varying charging behaviors and possible faults in faults in some chargers. To address the gap, a novel Multi-Agent Reinforcement Learning (MARL) approach is proposed treating each charger to be an agent and coordinate all the agents in the EV charging station with solar photovoltaics in a more realistic scenario, where system faults may occur. A Long Short-Term Memory (LSTM) network is incorporated in the MARL algorithm to extract temporal features from time-series. Additionally, a dense reward mechanism is designed for training the agents in the MARL algorithm to improve EV charging experience. Through validation on a real-world dataset, we show that our approach is robust against system uncertainties and faults and also effective in minimizing EV charging costs and maximizing charging service satisfaction.
Sampled-data Systems: Stability, Contractivity and Single-iteration Suboptimal MPC
Authors: Yiting Chen, Francesco Bullo, Emiliano Dall'Anese
Date: 2025-05-23 00:00:00
Tasks: Model Predictive Control

This paper analyzes the stability of interconnected continuous-time (CT) and discrete-time (DT) systems coupled through sampling and zero-order hold mechanisms. The DT system updates its output at regular intervals $T>0$ by applying an $n$-fold composition of a given map. This setup is motivated by online and sampled-data implementations of optimization-based controllers - particularly model predictive control (MPC) - where the DT system models $n$ iterations of an algorithm approximating the solution of an optimization problem. We introduce the concept of a reduced model, defined as the limiting behavior of the sampled-data system as $T \to 0^+$ and $n \to +\infty$. Our main theoretical contribution establishes that when the reduced model is contractive, there exists a threshold duration $T(n)$ for each iteration count $n$ such that the CT-DT interconnection achieves exponential stability for all sampling periods $T < T(n)$. Finally, under the stronger condition that both the CT and DT systems are contractive, we show exponential stability of their interconnection using a small-gain argument. Our theoretical results provide new insights into suboptimal MPC stability, showing that convergence guarantees hold even when using a single iteration of the optimization algorithm - a practically significant finding for real-time control applications.
Analysis on Energy Efficiency of RIS-Assisted Multiuser Downlink Near-Field Communications
Authors: Wei Wang, Xiaoyu Ou, Zhihan Ren, Waqas bin Abbas, Shuping Dang, Angela Doufexi, Mark A. Beach
Date: 2025-05-23 00:00:00

In this paper, we focus on the energy efficiency (EE) optimization and analysis of reconfigurable intelligent surface (RIS)-assisted multiuser downlink near-field communications. Specifically, we conduct a comprehensive study on several key factors affecting EE performance, including the number of RIS elements, the types of reconfigurable elements, reconfiguration resolutions, and the maximum transmit power. To accurately capture the power characteristics of RISs, we adopt more practical power consumption models for three commonly used reconfigurable elements in RISs: PIN diodes, varactor diodes, and radio frequency (RF) switches. These different elements may result in RIS systems exhibiting significantly different energy efficiencies (EEs), even when their spectral efficiencies (SEs) are similar. Considering discrete phases implemented at most RISs in practice, which makes their optimization NP-hard, we develop a nested alternating optimization framework to maximize EE, consisting of an outer integer-based optimization for discrete RIS phase reconfigurations and a nested non-convex optimization for continuous transmit power allocation within each iteration. Extensive comparisons with multiple benchmark schemes validate the effectiveness and efficiency of the proposed framework. Furthermore, based on the proposed optimization method, we analyze the EE performance of RISs across different key factors and identify the optimal RIS architecture yielding the highest EE.
Transient Slack Capability
Authors: Rodrigo Bernal, Federico Milano
Date: 2025-05-23 00:00:00

This paper introduces the concept of Transient Slack Capability (TSC), a set of three necessary device-level conditions to ensure stability under sustained power perturbations. TSC states that a device must (1) possess sufficient stored energy; (2) a controlled input power; and (3) maintain internal energy balance and synchronization. The paper shows that the relation among the time-scales of storage, control, and power perturbation is at the core of the TSC concept. Using the port-Hamiltonian (PH) framework, these conditions are formalized and validated via simulations on an adapted model of the WSCC 9-bus system. Case studies demonstrate that TSC is achievable in both Grid-Following (GFL) and Grid-Forming (GFM) converter control schemes, provided the conditions above are satisfied. Sensitivity analysis serves to identify storage and power reserve requirements to meet Conditions 1 and 2; the impact of converter current limiters on Condition 3; and inertia-less solutions able to achieve TSC.
Preliminary Characterization of Bio-inspired Dog-Nose Sampler for Aerosol Detection
Authors: Yahya Naveed, Julia Gersey, Pei Zhang
Date: 2025-05-23 00:00:00

Before aerosols can be sensed, sampling technologies must capture the particulate matter of interest. To that end, for systems deployed in open environments where the location of the aerosol is unknown, extending the reach of the sampler could lessen the precision required in sensor placement or reduce the number of sensors required for full spatial coverage. Inspired by the sensitivity of the canine olfactory system, this paper presents a rudimentary sampler that mimics the air flow of a dog's nose. The design consists of speed-controlled inhalation jets, as well as exhalation jets that are angled down and to the side. We tested this design on volatile organic compounds (VOC) in a small number of scenarios to validate the concept and understand how the system behaves. We show that in preliminary testing this dog-nose setup provides improvements over passive and solely inhalation sensing.
Selection Mechanisms for Sequence Modeling using Linear State Space Models
Authors: Umberto Casti, Sandro Zampieri, Fabio Pasqualetti
Date: 2025-05-23 00:00:00
Tasks: Fault Detection, Language Modeling, Language Modelling, Mamba, State Space Models

Recent advancements in language modeling tasks have been driven by architectures such as Transformers and, more recently, by Selective State Space Models (SSMs). In this paper, we introduce an alternative selection mechanism inspired by control theory methodologies. Specifically, we propose a novel residual generator for selection, drawing an analogy to fault detection strategies in Linear Time-Invariant (LTI) systems. Unlike Mamba, which utilizes Linear Time-Varying (LTV) systems, our approach combines multiple LTI systems, preserving their beneficial properties during training while achieving comparable selectivity. To evaluate the effectiveness of the proposed architecture, we test its performance on synthetic tasks. While these tasks are not inherently critical, they serve as benchmarks to test the selectivity properties of different cores architecture. This work highlights the potential of integrating theoretical insights with experimental advancements, offering a complementary perspective to deep learning innovations at the intersection of control theory and machine learning.
Sufficient Conditions for Detectability of Approximately Discretized Nonlinear Systems
Authors: Seth Siriya, Julian D. Schiller, Victor G. Lopez, Matthias A. Müller
Date: 2025-05-23 00:00:00

In many sampled-data applications, observers are designed based on approximately discretized models of continuous-time systems, where usually only the discretized system is analyzed in terms of its detectability. In this paper, we show that if the continuous-time system satisfies certain linear matrix inequality (LMI) conditions, and the sampling period of the discretization scheme is sufficiently small, then the whole family of discretized systems (parameterized by the sampling period) satisfies analogous discrete-time LMI conditions that imply detectability. Our results are applicable to general discretization schemes, as long as they produce approximate models whose linearizations are in some sense consistent with the linearizations of the continuous-time ones. We explicitly show that the Euler and second-order Runge-Kutta methods satisfy this condition. A batch-reactor system example is provided to highlight the usefulness of our results from a practical perspective.
TransDF: Time-Series Forecasting Needs Transformed Label Alignment
Authors: Hao Wang, Licheng Pan, Zhichao Chen, Xu Chen, Qingyang Dai, Lei Wang, Haoxuan Li, Zhouchen Lin
Date: 2025-05-23 00:00:00
Tasks: Time Series, Time Series Forecasting

Training time-series forecasting models presents unique challenges in designing effective learning objectives. Existing methods predominantly utilize the temporal mean squared error, which faces two critical challenges: (1) label autocorrelation, which leads to bias from the label sequence likelihood; (2) excessive amount of tasks, which increases with the forecast horizon and complicates optimization. To address these challenges, we propose Transform-enhanced Direct Forecast (TransDF), which transforms the label sequence into decorrelated components with discriminated significance. Models are trained to align the most significant components, thereby effectively mitigating label autocorrelation and reducing task amount. Extensive experiments demonstrate that TransDF achieves state-of-the-art performance and is compatible with various forecasting models. Code is available at https://anonymous.4open.science/r/TransDF-88CF.
Periodic Regulation of Linear Time-Delay Systems via Youla-Kučera Parametrization
Authors: Can Kutlu Yüksel, Tomáš Vyhlídal, Silviu-Iulian Niculescu
Date: 2025-05-23 00:00:00

The paper proposes an alternative way to achieve the Internal Model Principle (IMP) in contrast to the standard way, where a model of the signal one wishes to track/reject is directly substituted into the closed-loop. The proposed alternative approach relies on an already-existing stabilizing controller, which can be further augmented with a Youla-Ku\v{c}era parameter to let it implicitly admit a model of a signal without altering its stabilizing feature. Thus, with the proposed method, the standard design steps of realizing IMP are reversed. The validity and potential of the proposed approach are demonstrated by considering three different types of time-delay systems. It is shown through simulations that all considered unstable systems, despite the infinite-dimensional closed-loop, can be straightforwardly periodically regulated by augmenting their stabilizing PI controllers. Thanks to a specifically chosen structure for the Youla-Ku\v{c}era parameter, the required tuning can be done by solving a set of linear equations.
HRSim: An agent-based simulation platform for high-capacity ride-sharing services
Authors: Wang Chen, Hongzheng Shi, Jintao Ke
Date: 2025-05-23 00:00:00

The rapid growth of ride-sharing services presents a promising solution to urban transportation challenges, such as congestion and carbon emissions. However, developing efficient operational strategies, such as pricing, matching, and fleet management, requires robust simulation tools that can replicate real-world dynamics at scale. Existing platforms often lack the capacity, flexibility, or open-source accessibility needed to support large-scale, high-capacity ride-sharing services. To address these gaps, we introduce HRSim, an open-source, agent-based High-capacity Ride-sharing Simulator. HRSim integrates real-world road networks and demand data to simulate dynamic ride-sharing operations, including pricing, routing, matching, and repositioning. Its module design supports both ride-sharing and solo-hailing service modes. Also, it includes a visualization module for real-time performance analysis. In addition, HRSim incorporates integer linear programming and heuristic algorithms, which can achieve large-scale simulations of high-capacity ride-sharing services. Applications demonstrate HRSim's utility in various perspectives, including quantifying carbon emissions, scaling ride-sharing performance, evaluating new strategies, etc. By bridging the gap between theoretical research and practical implementation, HRSim serves as a versatile testbed for policymakers and transportation network companies to optimize ride-sharing systems for efficiency and sustainability.
Risk-based approach to the Optimal Transmission Switching problem
Authors: Benoît Jeanson, Simon H. Tindemans
Date: 2025-05-23 00:00:00

This paper deals with the secure Optimal Transmission Switching (OTS) problem in situations where the TSO is forced to accept the risk that some contingencies may result in the de-energization of parts of the grid to avoid the violation of operational limits. This operational policy, which mainly applies to subtransmission systems, is first discussed. Then, a model of that policy is proposed that complements the classical MILP model of the N-1 secure OTS problem. It comprises a connectivity and notably a partial grid loss analysis for branch outage contingencies. Finally, its application to the IEEE 14-bus system is presented. Solutions similar to those observed in operation are reached by the algorithm, notably revealing the preventive-openings-cascade phenomenon.
Enhancing AI System Resiliency: Formulation and Guarantee for LSTM Resilience Based on Control Theory
Authors: Sota Yoshihara, Ryousuke Yamamoto, Hiroyuki Kusumoto, Masanari Shimura
Date: 2025-05-23 00:00:00

This research proposes methods for formulating and guaranteeing the resilience of long short-term memory (LSTM) networks, which can serve as a key technology in AI system quality assurance. We introduce a novel methodology applying incremental input-to-state stability ($\delta$ISS) to mathematically define and evaluate the resilience of LSTM against input perturbations. Key achievements include the development of a data-independent evaluation method and the demonstration of resilience control through adjustments to training parameters. This research presents concrete solutions to AI quality assurance from a control theory perspective, which can advance AI applications in control systems.
ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics
Authors: Haoran Li, Muhao Guo, Yang Weng, Marija Ilic, Guangchun Ruan
Date: 2025-05-23 00:00:00

Non-stationary power system dynamics, influenced by renewable energy variability, evolving demand patterns, and climate change, are becoming increasingly complex. Accurately capturing these dynamics requires a model capable of adapting to environmental factors. Traditional models, including Recurrent Neural Networks (RNNs), lack efficient mechanisms to encode external factors, such as time or environmental data, for dynamic adaptation. To address this, we propose the External Adaptive RNN (ExARNN), a novel framework that integrates external data (e.g., weather, time) to continuously adjust the parameters of a base RNN. ExARNN achieves this through a hierarchical hypernetwork design, using Neural Controlled Differential Equations (NCDE) to process external data and generate RNN parameters adaptively. This approach enables ExARNN to handle inconsistent timestamps between power and external measurements, ensuring continuous adaptation. Extensive forecasting tests demonstrate ExARNN's superiority over established baseline models.
Autonomous Circular Drift Control for 4WD-4WS Vehicles Without Precomputed Drifting Equilibrium
Authors: Yue Xiao, Yi He, Yaqing Zhang, Xin Lin, Ming Zhang
Date: 2025-05-23 00:00:00

Under extreme conditions, autonomous drifting enables vehicles to follow predefined paths at large slip angles, significantly enhancing the control system's capability to handle hazardous scenarios. Four-wheel-drive and four-wheel-steering (4WD-4WS) vehicles, which have been extensively studied, offer superior path-following precision and enhanced maneuverability under challenging driving conditions. In this paper, a hierarchical drifting controller is proposed for 4WD-4WS vehicles to track both path and velocity without relying on precomputed drifting equilibrium. The controller is structured into two layers: a trajectory tracking layer and an actuator regulation layer. The first layer generates the desired tire forces in the vehicle body frame, while the second layer converts these desired tire forces into steering angle commands and torque commands for the front and rear motors. The effectiveness and robustness of the proposed controller are validated through simulation.
A Dynamic Phasor Framework for Analysis of Grid-Forming Converter Connected to Series-Compensated Line
Authors: Fiaz Hossain, Nilanjan Ray Chaudhuri
Date: 2025-05-23 00:00:00

A dynamic phasor (DP) framework for time-domain and frequency-domain analyses of grid-forming converters (GFCs) connected to series-compensated transmission lines is proposed. The proposed framework can capture the behavior of GFCs subjected to unbalanced short circuit faults in presence of different current limiting strategies. Moreover, the linearizability and time invariance of this framework allows us to perform eigen decomposition, which is a powerful tool for root-cause analysis and control design. We show that a certain degree of series compensation may result in poorly-damped oscillations in presence of the grid-forming converter. A participation factor analysis using the DP model reveals that the point of interconnection voltage angle is dominant in this mode. Eigenvalue sensitivity analysis of controller parameters shows that reducing the power-frequency droop coefficient is most effective in stabilizing the poorly-damped mode. Detailed validation with electromagnetic transient model demonstrates the accuracy of the proposed framework.
State of health prediction of lithium-ion batteries for driving conditions based on full parameter domain sparrow search algorithm and dual-module bidirectional gated recurrent unit
Authors: Jie Wen, Chenyu Jia, Guangshu Xia
Date: 2025-05-23 00:00:00

Aiming at the state of health (SOH) prediction of lithium-ion batteries (LiBs) for electric vehicles (EVs), this paper proposes a fusion model of a dual-module bidirectional gated recurrent unit (BiGRU) and sparrow search algorithm (SSA) with full parameter domain optimization. With the help of Spearman correlation analysis and ablation experiments, the indirect health indicator (HI) that can characterize the battery degradation is extracted first based on the incremental capacity (IC) curves of the Oxford battery dataset, which simulates the driving conditions. On this basis, the filtered one-dimensional HI is inputted into the dual-module BiGRU for learning the pre- and post-textual information of the input sequence and extracting the sequence features. In order to combine the different hyperparameters in the dual-module BiGRU, SSA is used to optimize the hyperparameters in the full parameter domain. The proposed SSA-BiGRU model combines the advantages and structures of SSA and BiGRU to achieve the highly accurate SOH prediction of LiBs. Studies based on the Oxford battery dataset have shown that the SSA-BiGRU model has higher accuracy, better robustness and generalization ability. Moreover, the proposed SSA-BiGRU model is tested on a real road-driven EV charging dataset and accurate SOH prediction are obtained.
Control of Renewable Energy Communities using AI and Real-World Data
Authors: Tiago Fonseca, Clarisse Sousa, Ricardo Venâncio, Pedro Pires, Ricardo Severino, Paulo Rodrigues, Pedro Paiva, Luis Lino Ferreira
Date: 2025-05-22 00:00:00
Tasks: Data Integration, energy management, Reinforcement Learning (RL), Scheduling

The electrification of transportation and the increased adoption of decentralized renewable energy generation have added complexity to managing Renewable Energy Communities (RECs). Integrating Electric Vehicle (EV) charging with building energy systems like heating, ventilation, air conditioning (HVAC), photovoltaic (PV) generation, and battery storage presents significant opportunities but also practical challenges. Reinforcement learning (RL), particularly MultiAgent Deep Deterministic Policy Gradient (MADDPG) algorithms, have shown promising results in simulation, outperforming heuristic control strategies. However, translating these successes into real-world deployments faces substantial challenges, including incomplete and noisy data, integration of heterogeneous subsystems, synchronization issues, unpredictable occupant behavior, and missing critical EV state-of-charge (SoC) information. This paper introduces a framework designed explicitly to handle these complexities and bridge the simulation to-reality gap. The framework incorporates EnergAIze, a MADDPG-based multi-agent control strategy, and specifically addresses challenges related to real-world data collection, system integration, and user behavior modeling. Preliminary results collected from a real-world operational REC with four residential buildings demonstrate the practical feasibility of our approach, achieving an average 9% reduction in daily peak demand and a 5% decrease in energy costs through optimized load scheduling and EV charging behaviors. These outcomes underscore the framework's effectiveness, advancing the practical deployment of intelligent energy management solutions in RECs.