pwc-archive/papers-with-abstracts huggingface dataset:
PEDRO-V: From a concurrent engineering case study to a promising phase zero mission definition
Authors: Domenico D'Auria, Arianna Rigo, Luca Niero, Andrei-Toma Stoica, Vito Costantini, Pasquale Castellano, Zsofia Zita Szilagyi, Nishani Vijayakumaran, Ella Toppari, Stefano Schiano, Marco Adorno, Matteo Matrone, Chiara Tulli, Jan Kurowski, Leo Bougault, Argenziano Francesco, Antignano Claudia, Theodoros Roumanis, Victoria Kossack, Spyridon Giuvalas
Date: 2025-02-19 00:00:00

Each year, the European Space Agency (ESA) organizes challenges for university students, from BSc to PhD levels. The ESA Concurrent Engineering Challange 2024 was hosted by four Concurrent Design Facilites (CDF) across Europe: ESEC Galazia, ISAE SUPAERO, the University of Athens, and the University of Portsmouth. A total of 102 students participated in the event. Over five days, students worked on a feasibility study for a space mission, simulating ESA's design session at ESTEC, the ESA headquarters. Students were divided into specializes groups based on their backgrounds, reflecting ESA's concurrent engineering teams. This paper discusses the design of subsystems by students, their trade-off results, and the outcomes of the CDF study. It highlights the effectiveness of concurrent engineering, which enabled rapid and efficient results even from non-esxpert teams. The future development roadmap and lessons learned are also presented. The students used CDP4-Comet software within the replicated ESA CDF, resulting in the PEDRO-V mission proposal: Planetary Exploration Deployment and Research Operation - Venus. The teams collaboratively defined the Concept of Operations, identified actors, worst-case scenarios, use cases, and activities. Their output included a list of requirements, a draft product breakdown structure, and key subsystems information. The concurrent engineering process led to continuous improvement and convergence of key parameters. This approach proved to be effective by aligning different teams' solutions and comparing them to similar missions. The PEDRO-V mission feasibility was confirmed, demonstrating the potential of concurrent engineering in accademic settings for space missions. (summarized with AI)
Hierarchical RL-MPC for Demand Response Scheduling
Authors: Maximilian Bloor, Ehecatl Antonio del Rio Chanona, Calvin Tsay
Date: 2025-02-19 00:00:00
Tasks: Model Predictive Control, Reinforcement Learning (RL), Scheduling

This paper presents a hierarchical framework for demand response optimization in air separation units (ASUs) that combines reinforcement learning (RL) with linear model predictive control (LMPC). We investigate two control architectures: a direct RL approach and a control-informed methodology where an RL agent provides setpoints to a lower-level LMPC. The proposed RL-LMPC framework demonstrates improved sample efficiency during training and better constraint satisfaction compared to direct RL control. Using an industrial ASU case study, we show that our approach successfully manages operational constraints while optimizing electricity costs under time-varying pricing. Results indicate that the RL-LMPC architecture achieves comparable economic performance to direct RL while providing better robustness and requiring fewer training samples to converge. The framework offers a practical solution for implementing flexible operation strategies in process industries, bridging the gap between data-driven methods and traditional control approaches.
Multi-Target Radar Search and Track Using Sequence-Capable Deep Reinforcement Learning
Authors: Jan-Hendrik Ewers, David Cormack, Joe Gibbs, David Anderson
Date: 2025-02-19 00:00:00
Tasks: Deep Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning

The research addresses sensor task management for radar systems, focusing on efficiently searching and tracking multiple targets using reinforcement learning. The approach develops a 3D simulation environment with an active electronically scanned array radar, using a multi-target tracking algorithm to improve observation data quality. Three neural network architectures were compared including an approach using fated recurrent units with multi-headed self-attention. Two pre-training techniques were applied: behavior cloning to approximate a random search strategy and an auto-encoder to pre-train the feature extractor. Experimental results revealed that search performance was relatively consistent across most methods. The real challenge emerged in simultaneously searching and tracking targets. The multi-headed self-attention architecture demonstrated the most promising results, highlighting the potential of sequence-capable architectures in handling dynamic tracking scenarios. The key contribution lies in demonstrating how reinforcement learning can optimize sensor management, potentially improving radar systems' ability to identify and track multiple targets in complex environments.
Implementation of an IEEE 802.11ax-Based Maritime Mesh Network in the Red Sea
Authors: Yingquan Li, Jiajie Xu, Narek Khachatrian, Mohamed-Slim Alouini
Date: 2025-02-19 00:00:00

In this article, we explore the limitations of satellite phones in meeting the communication needs of fishermen operating in the Red Sea. We propose AX-MMN, a maritime mesh network based on the IEEE 802.11ax standard, to address these shortcomings of satellite phones and outline AX-MMN's system architecture. To validate the performance of AX-MMN, we conduct extensive real-world experiments, demonstrating its potential to enhance maritime connectivity significantly. We also discuss the broader benefits of AX-MMN, particularly for fishermen in underdeveloped East African countries bordering the Red Sea, emphasizing its capacity to improve their communication capabilities and overall quality of life.
Kernel Mean Embedding Topology: Weak and Strong Forms for Stochastic Kernels and Implications for Model Learning
Authors: Naci Saldi, Serdar Yuksel
Date: 2025-02-19 00:00:00

We introduce a novel topology, called Kernel Mean Embedding Topology, for stochastic kernels, in a weak and strong form. This topology, defined on the spaces of Bochner integrable functions from a signal space to a space of probability measures endowed with a Hilbert space structure, allows for a versatile formulation. This construction allows one to obtain both a strong and weak formulation. (i) For its weak formulation, we highlight the utility on relaxed policy spaces, and investigate connections with the Young narrow topology and Borkar (or \( w^* \))-topology, and establish equivalence properties. We report that, while both the \( w^* \)-topology and kernel mean embedding topology are relatively compact, they are not closed. Conversely, while the Young narrow topology is closed, it lacks relative compactness. (ii) We show that the strong form provides an appropriate formulation for placing topologies on spaces of models characterized by stochastic kernels with explicit robustness and learning theoretic implications on optimal stochastic control under discounted or average cost criteria. (iii) We show that this topology possesses several properties making it ideal to study optimality, approximations, robustness and continuity properties. In particular, the kernel mean embedding topology has a Hilbert space structure, which is particularly useful for approximating stochastic kernels through simulation data.
Empirical Study of Dynamic Regret in Online Model Predictive Control for Linear Time-Varying Systems
Authors: Nhat M. Nguyen
Date: 2025-02-19 00:00:00
Tasks: Model Predictive Control, Prediction

Model Predictive Control (MPC) is a widely used technique for managing timevarying systems, supported by extensive theoretical analysis. While theoretical studies employing dynamic regret frameworks have established robust performance guarantees, their empirical validation remains sparse. This paper investigates the practical applicability of MPC by empirically evaluating the assumptions and theoretical results proposed by Lin et al. [2022]. Specifically, we analyze the performance of online MPC under varying prediction errors and prediction horizons in Linear Time-Varying (LTV) systems. Our study examines the relationship between dynamic regret, prediction errors, and prediction horizons, providing insights into the trade-offs involved. By bridging theory and practice, this work advances the understanding and application of MPC in real-world scenarios.
From Maneuver to Mishap: A Systematic Literature Review on U-Turn Safety Risks
Authors: Syed Aaqib Javed, Anannya Ghosh Tusti, Biplov Pandey, Subasish Das
Date: 2025-02-18 00:00:00
Tasks: Systematic Literature Review

Understanding the impacts of U-turn configurations on intersection safety and traffic operations is essential for developing effective strategies to enhance road safety and efficiency. Extensive research has been conducted to investigate the role of geometric designs, driver behavior, and advanced technologies in mitigating crash risks and improving traffic flow at U-turn facilities. By synthesizing this collective body of work through the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), this paper provides a valuable resource for transportation professionals, policymakers, and researchers seeking evidence-based solutions. This systematic review draws on studies from diverse traffic environments and regional contexts, focusing on innovative design interventions, such as restricted crossing U-turns (RCUTs) and median U-turn intersections (MUTs), as well as integrated strategies leveraging technological advancements. By presenting a comprehensive analysis of U-turn-related challenges and opportunities, this review contributes to advancing transportation safety research and guiding the development of adaptive strategies tailored to varied traffic conditions and evolving technologies.
A Note on Structural Controllability and Observability Indices
Authors: Yuan Zhang, Ranbo Cheng, Ziyuan Luo, Yuanqing Xia
Date: 2025-02-19 00:00:00

In this note, we investigate the structural controllability and observability indices of structured systems. We provide counter-examples showing that an existing graph-theoretic characterization for the structural controllability index (SCOI) may not hold, even for systems with self-loop at every state node. We further demonstrate that this characterization actually provides upper bounds, and extend them to new graph-theoretic characterizations applicable to systems that are not necessarily structurally controllable. Additionally, we reveal that an existing method may fail to obtain the exact SCOI. Consequently, complete graph-theoretic characterizations and polynomial-time computation of SCOI remain open. Given this, we present an efficiently computable tight lower bound, whose tightness is validated by numerical simulations. All these results apply to the structural observability index by the duality between controllability and observability.
Observability-Blocking Controls for Double-Integrator and Higher Order Integrator Networks
Authors: Joseph D. Tran, Abdullah Al Maruf
Date: 2025-02-18 00:00:00
Tasks: Blocking

The design of state-feedback controls to block observability at remote nodes is studied for double integrator network (DIN) and higher order integrator network models. A preliminary design algorithm is presented first for DIN that requires $m+2$ actuation nodes to block observability for the measurement obtained from a set of $m$ nodes. The algorithm is based on eigenstructure assignment technique and leverages the properties of the eigenvectors in DIN. Next, the topological structure of the network is exploited to reduce the number of controllers required for blocking observability. The number of actuation nodes in sparser design depends on the cardinality of a cutset separating the actuation and measurement locations. Later, the design principles are generalized for blocking observability in $N$-th order integrator network models.
Automated Linear Parameter-Varying Modeling of Nonlinear Systems: A Global Embedding Approach
Authors: E. Javier Olucha, Patrick J. W. Koelewijn, Amritam Das, Roland Tóth
Date: 2025-02-18 00:00:00

In this paper, an automated Linear Parameter-Varying (LPV) model conversion approach is proposed for nonlinear dynamical systems. The proposed method achieves global embedding of the original nonlinear behavior of the system by leveraging the second fundamental theorem of calculus to factorize matrix function expressions without any approximation. The implementation of the proposed method in the LPVcore toolbox for Matlab is discussed, and its performance is showcased on a comprehensive example of automated LPV model conversion of an unbalanced disk system, which is then used to design an LPV controller that is deployed on the original nonlinear system. In addition, the conversion capabilities are further demonstrated by obtaining an LPV embedding of a three-degree-of-freedom control moment gyroscope. All software implementations are available at www.lpvcore.net.
Pricing is All You Need to Improve Traffic Routing
Authors: Yu Tang, Kaan Ozbay, Li Jin
Date: 2025-02-18 00:00:00
Tasks: All

We investigate the design of pricing policies that enhance driver adherence to route guidance, ensuring effective routing control. The major novelty lies in that we adopt a Markov chain to model drivers' compliance rates conditioned on both traffic states and tolls. By formulating the managed traffic network as a nonlinear stochastic dynamical system, we can quantify in a more realistic way the impacts of driver route choices and thus determine appropriate tolls. Specially, we focus on a network comprised of one corridor and one local street. We assume that a reasonable routing policy is specified in advance. However, drivers could be reluctant to be detoured. Thus a fixed toll is set on the corridor to give drivers incentives to choose the local street. We evaluate the effectiveness of the given routing and pricing policies via stability analysis. We suggest using the stability and instability conditions to establish lower and upper bounds on throughput. This allows us to select suitable tolls that maximize these bounds.
Network-Realized Model Predictive Control Part II: Distributed Constraint Management
Authors: Andrei Sperilă, Alessio Iovine, Sorin Olaru, Patrick Panciatici
Date: 2025-02-18 00:00:00
Tasks: Management, Model Predictive Control

A two-layer control architecture is proposed, which promotes scalable implementations for model predictive controllers. The top layer acts as both reference governor for the bottom layer, and as a feedback controller for the regulated network. By employing set-based methods, global theoretical guarantees are obtained by enforcing local constraints upon the variables of the network and of the first layer's implementation. The proposed technique offers recursive feasibility guarantees as one of its central features, and the expressions of the resulting predictive strategies bear a striking resemblance to classical formulations from model predictive control literature, allowing for flexible and easily customizable implementations.
Network-Realized Model Predictive Control Part I: NRF-Enabled Closed-loop Decomposition
Authors: Andrei Sperilă, Alessio Iovine, Sorin Olaru, Patrick Panciatici
Date: 2025-02-18 00:00:00
Tasks: Model Predictive Control

A two-layer control architecture is proposed, which promotes scalable implementations for model predictive controllers. The top layer acts as both reference governor for the bottom layer, and as a feedback controller for the regulated network. By employing set-based methods, global theoretical guarantees are obtained by enforcing local constraints upon the variables of the network and of the first layer's implementation. The proposed technique offers recursive feasibility guarantees as one of its central features, and the expressions of the resulting predictive strategies bear a striking resemblance to classical formulations from model predictive control literature, allowing for flexible and easily customizable implementations.
Radar Network for Gait Monitoring: Technology and Validation
Authors: Ignacio E. López-Delgado, Víctor Navarro-López, Francisco Grandas-Pérez, Juan I. Godino-Llorente, Jesús Grajal
Date: 2025-02-18 00:00:00

In recent years, radar-based devices have emerged as an alternative approach for gait monitoring. However, the radar configuration and the algorithms used to extract the gait parameters often differ between contributions, lacking a systematic evaluation of the most appropriate setup. Additionally, radar-based studies often exclude motorically impaired subjects, leaving it unclear whether the existing algorithms are applicable to such populations. In this paper, a radar network is developed and validated by monitoring the gait of five healthy individuals and three patients with Parkinson's disease. Six configurations and four algorithms were compared using Vicon as ground-truth to determine the most appropriate solution for gait monitoring. The best results were obtained using only three nodes: two oriented towards the feet and one towards the torso. The most accurate stride velocity and distance in the state of the art were obtained with this configuration. Moreover, we show that analyzing the feet velocity increases the reliability of the temporal parameters, especially with aged or motorically impaired subjects. The contribution is significant for the implementation of radar networks in clinical and domestic environments, as it addresses critical aspects concerning the radar network configuration and algorithms.
A Graph-Enhanced Deep-Reinforcement Learning Framework for the Aircraft Landing Problem
Authors: Vatsal Maru
Date: 2025-02-18 00:00:00
Tasks: Deep Reinforcement Learning, Management, Scheduling

The Aircraft Landing Problem (ALP) is one of the challenging problems in aircraft transportation and management. The challenge is to schedule the arriving aircraft in a sequence so that the cost and delays are optimized. There are various solution approaches to solving this problem, most of which are based on operations research algorithms and meta-heuristics. Although traditional methods perform better on one or the other factors, there remains a problem of solving real-time rescheduling and computational scalability altogether. This paper presents a novel deep reinforcement learning (DRL) framework that combines graph neural networks with actor-critic architectures to address the ALP. This paper introduces three key contributions: A graph-based state representation that efficiently captures temporal and spatial relationships between aircraft, a specialized actor-critic architecture designed to handle multiple competing objectives in landing scheduling, and a runway balance strategy that ensures efficient resource utilization while maintaining safety constraints. The results show that the trained algorithm can be tested on different problem sets and the results are competitive to operation research algorithms. The experimental results on standard benchmark data sets demonstrate a 99.95% reduction in computational time compared to Mixed Integer Programming (MIP) and 38% higher runway throughput over First Come First Serve (FCFS) approaches. Therefore, the proposed solution is competitive to traditional approaches and achieves substantial advancements. Notably, it does not require retraining, making it particularly suitable for industrial deployment. The frameworks capability to generate solutions within 1 second enables real-time rescheduling, addressing critical requirements of air traffic management.
A Novel Gain Modeling Technique for LLC Resonant Converters based on The Hybrid Deep-Learning/GMDH Neural Network
Authors: Parham Mohammadi
Date: 2025-02-18 00:00:00
Tasks: Deep Learning

This paper presents a novel hybrid approach for modeling the voltage gain of LLC resonant converters by combining deep-learning neural networks with the polynomial based Group Method of Data Handling (GMDH). While deep learning offers high accuracy in predicting nonlinear converter behavior, it produces complex network models. GMDH neural networks, in contrast, yield simpler algebraic equations that can be more convenient in converter design. By training a deep network on data from an FPGA based real time simulator and then using the network s predictions to train a GMDH model, the proposed hybrid method achieves both high accuracy and design friendly simplicity. Experimental results show significant improvements over traditional methods such as First Harmonic Approximation (FHA) and frequency domain corrections, particularly for wide operating ranges.
Stochastic Real-Time Deception in Nash Equilibrium Seeking for Games with Quadratic Payoffs
Authors: Michael Tang, Miroslav Krstic, Jorge Poveda
Date: 2025-02-17 00:00:00

In multi-agent autonomous systems, deception is a fundamental concept which characterizes the exploitation of unbalanced information to mislead victims into choosing oblivious actions. This effectively alters the system's long term behavior, leading to outcomes that may be beneficial to the deceiver but detrimental to victim. We study this phenomenon for a class of model-free Nash equilibrium seeking (NES) where players implement independent stochastic exploration signals to learn the pseudogradient flow. In particular, we show that deceptive players who obtain real-time measurements of other players' stochastic perturbation can incorporate this information into their own NES action update, consequentially steering the overall dynamics to a new operating point that could potentially improve the payoffs of the deceptive players. We consider games with quadratic payoff functions, as this restriction allows us to derive a more explicit formulation of the capabilities of the deceptive players. By leveraging results on multi-input stochastic averaging for dynamical systems, we establish local exponential (in probability) convergence for the proposed deceptive NES dynamics. To illustrate our results, we apply them to a two player quadratic game.
Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV
Authors: Allen M. Wang, Alessandro Pau, Cristina Rea, Oswin So, Charles Dawson, Olivier Sauter, Mark D. Boyer, Anna Vu, Cristian Galperti, Chuchu Fan, Antoine Merle, Yoeri Poels, Cristina Venturini, Stefano Marchioni, The TCV Team
Date: 2025-02-17 00:00:00
Tasks: Reinforcement Learning (RL)

The rampdown in tokamak operations is a difficult to simulate phase during which the plasma is often pushed towards multiple instability limits. To address this challenge, and reduce the risk of disrupting operations, we leverage recent advances in Scientific Machine Learning (SciML) to develop a neural state-space model (NSSM) that predicts plasma dynamics during Tokamak \`a Configuration Variable (TCV) rampdowns. By integrating simple physics structure and data-driven models, the NSSM efficiently learns plasma dynamics during the rampdown from a modest dataset of 311 pulses with only five pulses in the reactor relevant high performance regime. The NSSM is parallelized across uncertainties, and reinforcement learning (RL) is applied to design trajectories that avoid multiple instability limits with high probability. Experiments at TCV ramping down high performance plasmas show statistically significant improvements in current and energy at plasma termination, with improvements in speed through continuous re-training. A predict-first experiment, increasing plasma current by 20\% from baseline, demonstrates the NSSM's ability to make small extrapolations with sufficient accuracy to design trajectories that successfully terminate the pulse. The developed approach paves the way for designing tokamak controls with robustness to considerable uncertainty, and demonstrates the relevance of the SciML approach to learning plasma dynamics for rapidly developing robust trajectories and controls during the incremental campaigns of upcoming burning plasma tokamaks.