pwc-archive/papers-with-abstracts huggingface dataset:
Joint System Modeling Approach for Fault Simulation of Start-er/Generator and Gas Generator in All-Electric APU
Authors: Haotian Mao, Yingqing Guo
Date: 2025-06-12 00:00:00
Tasks: All, Computational Efficiency, Fault Detection

This paper presents a joint system modeling approach for fault simulation of all-electric auxiliary power unit (APU), integrating starter/generator turn-to-turn short circuit (TTSC) faults with gas generator gas-path faults.To address challenges in electromechanical coupling, simulation precision and computational efficiency balance, we propose a multi-rate continuous-discrete hybrid simulation architecture. This architecture treats the starter/generator as a continuous system with variable step size in Simulink, while modeling the gas generator as a discrete system with fixed step size in a dynamic-link library (DLL) environment. For the starter/generator fault modeling, a multi-loop approach is deployed to accurately simulate TTSC faults. For the gas generator, we develop an improved GasTurb-DLL modeling method (IGDM) that enhances uncertainty modeling, state-space representation, and tool chain compatibility. Finally, the proposed methodology above was implemented in a case study based on the APS5000 all-electric APU structure and parameters. Model validation was conducted by comparing simulation results--covering steady-state, transients, healthy, and fault conditions--with reference data from third-party software and literature. The close agreement confirms both the model's accuracy and the effectiveness of our modeling methodology. This work establishes a modeling foundation for investigating the opportunities and challenges in fault detection and isolation (FDI) brought by the all electrification of the APU, including joint fault estimation and diagnosis, coupled electromechanical fault characteristics.
Analyzing the performance of a V2X-enhanced braking system in real-world crash situations
Authors: Jan Zimmermann, Jörg Mönnich, Michael Scherl, Ignacio Llatser, Florian Wildschütte, Frank Hofmann
Date: 2025-06-12 00:00:00

By using an automated braking system, such as the Automatic Emergency Brake (AEB), crashes can be avoided in situations where the driver is unaware of an imminent collision. However, conventional AEB systems detect potential collision adversaries with onboard sensor systems, such as radars and cameras, that may fail in non-line-of-sight situations. By leveraging vehicle-to-everything (V2X) communication, information regarding an approaching vehicle can be received by the ego vehicle at an early point in time, even if the opponent vehicle is occluded by a view obstruction. In this work, we consider a 2-stage braking cascade, consisting of a partial brake, triggered based on V2X information, and a sensor-triggered AEB. We evaluate its crash avoidance performance in real-world crash situations extracted from the German In-Depth Accident Study (GIDAS) database using an accident simulation framework. The results are compared against a sensor-triggered AEB system and a purely V2X-triggered partial brake. To further analyze the results, we identify the crash cause for each situation in which the brake function under test could not prevent the crash. The simulation results show a high added benefit of the V2X-enhanced braking systems compared to the exclusive use of visual-based sensor systems for automated collision prevention.
Predictive control of wastewater treatment plants as energy-autonomous water resource recovery facilities
Authors: Otacilio B. L. Neto, Michela Mulas, Iiro Harjunkoski, Francesco Corona
Date: 2025-06-12 00:00:00

This work proposes an automatic control solution for the operation of conventional wastewater treatment plants (WWTPs) as energy-autonomous water resource recovery facilities. We first conceptualize a classification of the quality of treated water for three resource recovery applications (environmental, industrial, and agricultural water reuse). We then present an output-feedback model predictive controller (Output MPC) that operates a plant to produce water of specific quality class, while also producing sufficient biogas to ensure nonpositive energy costs. The controller is demonstrated in the long-term operation of a full-scale WWTP subjected to typical influent loads and periodically changing quality targets. Our results provide a proof-of-concept on the energy-autonomous operation of existing wastewater treatment infrastructure with control strategies that are general enough to accommodate a wide range of resource recovery objectives.
Computationally Efficient Analytical Models of Frequency and Voltage in Low-Inertia Systems
Authors: Marena Trujillo, Amir Sajadi, Jonathan Shaw, Bri-Mathias Hodge
Date: 2025-06-07 00:00:00

In this paper, low-order models of the frequency and voltage response of mixed-generation, low-inertia systems are presented. These models are unique in their ability to efficiently and accurately model frequency and voltage dynamics without increasing the computational burden as the share of inverters is increased in a system. The models are validated against industry-grade electromagnetic transient simulation, compared to which the proposed models are several orders of magnitude faster. The accuracy and efficiency of the low-inertia frequency and voltage models makes them well suited for a variety of planning and operational studies, especially for multi-scenario and probabilistic studies, as well as for screening studies to establish impact zones based on the dynamic interactions between inverters and synchronous generators.
Synthesizing Min-Max Control Barrier Functions For Switched Affine Systems
Authors: Sara Kamali, Guillaume O. Berger, Sriram Sankaranarayanan
Date: 2025-06-12 00:00:00
Tasks: Combinatorial Optimization

We study the problem of synthesizing non-smooth control barrier functions (CBFs) for continuous-time switched affine systems. Switched affine systems are defined by a set of affine dynamical modes, wherein the control consists of a state-based switching signal that determines the current operating mode. The control barrier functions seek to maintain the system state inside a control invariant set that excludes a given set of unsafe states. We consider CBFs that take the form of pointwise minima and maxima over a finite set of affine functions. Our approach uses ideas from nonsmooth analysis to formulate conditions for min- and max- affine control barrier functions. We show how a feedback switching law can be extracted from a given CBF. Next, we show how to automate the process of synthesizing CBFs given a system description through a tree-search algorithm inspired by branch-and-cut methods from combinatorial optimization. Finally, we demonstrate our approach on a series of interesting examples of switched affine systems.
Learning-Based Stable Optimal Control for Infinite-Time Nonlinear Regulation Problems
Authors: Han Wang, Di wu, Lin Cheng, Shengping Gong, Xu Huang
Date: 2025-06-12 00:00:00

Infinite-time nonlinear optimal regulation control is widely utilized in aerospace engineering as a systematic method for synthesizing stable controllers. However, conventional methods often rely on linearization hypothesis, while recent learning-based approaches rarely consider stability guarantees. This paper proposes a learning-based framework to learn a stable optimal controller for nonlinear optimal regulation problems. First, leveraging the equivalence between Pontryagin Maximum Principle (PMP) and Hamilton-Jacobi-Bellman (HJB) equation, we improve the backward generation of optimal examples (BGOE) method for infinite-time optimal regulation problems. A state-transition-matrix-guided data generation method is then proposed to efficiently generate a complete dataset that covers the desired state space. Finally, we incorporate the Lyapunov stability condition into the learning framework, ensuring the stability of the learned optimal policy by jointly learning the optimal value function and control policy. Simulations on three nonlinear optimal regulation problems show that the learned optimal policy achieves near-optimal regulation control and the code is provided at https://github.com/wong-han/PaperNORC
Model Predictive Control-Based Optimal Energy Management of Autonomous Electric Vehicles Under Cold Temperatures
Authors: Shanthan Kumar Padisala, Satadru Dey
Date: 2025-06-11 00:00:00
Tasks: energy management, Management, Model Predictive Control

In autonomous electric vehicles (AEVs), battery energy must be judiciously allocated to satisfy primary propulsion demands and secondary auxiliary demands, particularly the Heating, Ventilation, and Air Conditioning (HVAC) system. This becomes especially critical when the battery is in a low state of charge under cold ambient conditions, and cabin heating and battery preconditioning (prior to actual charging) can consume a significant percentage of available energy, directly impacting the driving range. In such cases, one usually prioritizes propulsion or applies heuristic rules for thermal management, often resulting in suboptimal energy utilization. There is a pressing need for a principled approach that can dynamically allocate battery power in a way that balances thermal comfort, battery health and preconditioning, along with range preservation. This paper attempts to address this issue using real-time Model Predictive Control to optimize the power consumption between the propulsion, HVAC, and battery temperature preparation so that it can be charged immediately once the destination is reached.
Formalizing Neuromorphic Control Systems: A General Proposal and A Rhythmic Case Study
Authors: Taisia Medvedeva, Alessio Franci, Fernando Castaños
Date: 2025-06-11 00:00:00

Neuromorphic control is receiving growing attention due to the multifaceted advantages it brings over more classical control approaches, including: sparse and on-demand sensing, information transmission, and actuation; energy-efficient designs and realizations in neuromorphic hardware; event-based signal processing and control signal computation. However, a general control-theoretical formalization of what "neuromorphic control systems" are and how we can rigorously analyze, design, and control them is still largely missing. In this note, we suggest a possible path toward formalizing neuromorphic control systems. We apply the proposed framework to a rhythmic control case study and rigorously show how it has the potential to make neuromorphic control systems analysis and design amenable to mature control theoretical approaches like describing function analysis and harmonic balance, fast-slow analysis, discrete and hybrid systems, and robust optimization.
Probability-One Optimization of Generalized Rayleigh Quotient Sum For Multi-Source Generalized Total Least-Squares
Authors: Dominik Friml, Pavel Václavek
Date: 2025-06-11 00:00:00
Tasks: global-optimization

This paper addresses the global optimization of the sum of the Rayleigh quotient and the generalized Rayleigh quotient on the unit sphere. While various methods have been proposed for this problem, they do not guarantee convergence to the global maximizer. To overcome this limitation, we introduce a probability-one homotopy optimization method that, under certain conditions, guarantees convergence to the global maximizer. The proposed method is analyzed alongside state-of-the-art approaches through numerical experiments, evaluating their performance in terms of convergence speed and ability to reach the global maximizer. Furthermore, we demonstrate how this ties in with the multi-source Bayesian Generalized Total Least-Squares (B-GTLS) problem, illustrating its applicability.
Efficient Uncertainty Propagation with Guarantees in Wasserstein Distance
Authors: Eduardo Figueiredo, Steven Adams, Peyman Mohajerin Esfahani, Luca Laurenti
Date: 2025-06-10 00:00:00
Tasks: Computational Efficiency

In this paper, we consider the problem of propagating an uncertain distribution by a possibly non-linear function and quantifying the resulting uncertainty. We measure the uncertainty using the Wasserstein distance, and for a given input set of distributions close in the Wasserstein distance, we compute a set of distributions centered at a discrete distribution that is guaranteed to contain the pushforward of any distribution in the input set. Our approach is based on approximating a nominal distribution from the input set to a discrete support distribution for which the exact computation of the pushforward distribution is tractable, thus guaranteeing computational efficiency to our approach. Then, we rely on results from semi-discrete optimal transport and distributional robust optimization to show that for any $\epsilon > 0$ the error introduced by our approach can be made smaller than $\epsilon$. Critically, in the context of dynamical systems, we show how our results allow one to efficiently approximate the distribution of a stochastic dynamical system with a discrete support distribution for a possibly infinite horizon while bounding the resulting approximation error. We empirically investigate the effectiveness of our framework on various benchmarks, including a 10-D non-linear system, showing the effectiveness of our approach in quantifying uncertainty in linear and non-linear stochastic systems.
Minimal Order Recovery through Rank-adaptive Identification
Authors: Frédéric Zheng, Yassir Jedra, Alexandre Proutière
Date: 2025-06-10 00:00:00

This paper addresses the problem of identifying linear systems from noisy input-output trajectories. We introduce Thresholded Ho-Kalman, an algorithm that leverages a rank-adaptive procedure to estimate a Hankel-like matrix associated with the system. This approach optimally balances the trade-off between accurately inferring key singular values and minimizing approximation errors for the rest. We establish finite-sample Frobenius norm error bounds for the estimated Hankel matrix. Our algorithm further recovers both the system order and its Markov parameters, and we provide upper bounds for the sample complexity required to identify the system order and finite-time error bounds for estimating the Markov parameters. Interestingly, these bounds match those achieved by state-of-the-art algorithms that assume prior knowledge of the system order.
Optimization and Control Technologies for Renewable-Dominated Hydrogen-Blended Integrated Gas-Electricity System: A Review
Authors: Wenxin Liu, Jiakun Fang, Shichang Cui, Iskandar Abdullaev, Suyang Zhou, Xiaomeng Ai, Jinyu Wen
Date: 2025-06-11 00:00:00
Tasks: Scheduling

The growing coupling among electricity, gas, and hydrogen systems is driven by green hydrogen blending into existing natural gas pipelines, paving the way toward a renewable-dominated energy future. However, the integration poses significant challenges, particularly ensuring efficient and safe operation under varying hydrogen penetration and infrastructure adaptability. This paper reviews progress in optimization and control technologies for hydrogen-blended integrated gas-electricity system. First, key technologies and international demonstration projects are introduced to provide an overview of current developments. Besides, advances in gas-electricity system integration, including modeling, scheduling, planning and market design, are reviewed respectively. Then, the potential for cross-system fault propagation is highlighted, and practical methods for safety analysis and control are proposed. Finally, several possible research directions are introduced, aiming to ensure efficient renewable integration and reliable operation.
Large-scale LH2 pipeline infrastructure concept for airports
Authors: H. A. Krog, Y. Jooss, H. Fyhn, P. Nekså, I. Hjorth
Date: 2025-06-11 00:00:00

Infrastructure and processes for handling of liquid hydrogen (LH2) is needed to enable large-scale decarbonization of aviation with hydrogen aircraft. At large airports, pipeline and hydrant systems will be important for a mature hydrogen-powered air travel market. As the vaporization of LH2 is a challenge in fuel handling, the pipeline infrastructure must be designed and operated such that the fuel is subcooled. Through modelling and simulation of aircraft tanks refuelling by a pipeline infrastructure concept, it is found that continuous recycling of LH2 within the system is needed to maintain subcooling, and the pump operation is important for preventing flashing. With the proposed concept, some hydrogen vapor is formed in the aircraft tank, but the vapor can be utilised by hydrogen-powered ground support equipment.
Voltage-Controlled Oscillator and Memristor-Based Analog Computing for Solving Systems of Linear Equations
Authors: Hao Li, Rizwan S. Peerla, Frank Barrows, Francesco Caravelli, Bibhu Datta Sahoo
Date: 2025-06-11 00:00:00

Matrix computations have become increasingly significant in many data-driven applications. However, Moores law for digital computers has been gradually approaching its limit in recent years. Moreover, digital computers encounter substantial complexity when performing matrix computations and need a long time to finish the computations, and existing analog matrix computation schemes require a large chip area and power consumption. This paper proposes a linear algebra system of equations based on integrators, which features low power consumption, compact area, and fast computation time. Due to the simple structure of the ring oscillator, the ring oscillator-based integrator exhibits a compact area and low power consumption. Therefore, ring oscillator-based integrators are introduced into the linear algebra system of equations, and this system can be used to compute the linear algebra equations of the matrix with either positive or negative values. This paper provides a detailed analysis and verification of the proposed circuit structure. Compared to similar circuits, this work has significant advantages in terms of area, power consumption, and computation speed.
Integer-Clustering Optimization of Hydrogen and Battery EV Fleets Considering DERs
Authors: Sijia Geng, Thomas Lee, Dharik Mallapragada, Audun Botterud
Date: 2025-06-11 00:00:00
Tasks: Clustering

Electrified transportation leads to a tighter integration between transportation and energy distribution systems. In this work, we develop scalable optimization models to co-design hydrogen and battery electric vehicle (EV) fleets, distributed energy resources, and fast-charging and hydrogen-fueling infrastructure to efficiently meet transportation demands. A novel integer-clustering formulation is used for optimizing fleet-level EV operation while maintaining accurate individual vehicle dispatch, which significantly improves the computation efficiency with guaranteed performance. We apply the optimization model to Boston's public transit bus network using real geospatial data and cost parameters. Realistic insights are provided into the future evolution of coupled electricity-transportation-hydrogen systems, including the effects of electricity price structure, hydrogen fuel cost, carbon emission constraint, temperature effects on EV range, and distribution system upgrade cost.
Toward Low-Altitude Airspace Management and UAV Operations: Requirements, Architecture and Enabling Technologies
Authors: Guiyang Luo, Jinglin Li, Qixun Zhang, Zhiyong Feng, Quan Yuan, Yijing Lin, HUI ZHANG, Nan Cheng, Ping Zhang
Date: 2025-06-10 00:00:00
Tasks: Integrated sensing and communication, ISAC, Management

The low-altitude economy (LAE) is rapidly advancing toward intelligence, connectivity, and coordination, bringing new challenges in dynamic airspace management, unmanned aerial vehicle (UAV) operation, and security management. Existing systems remain fragmented and lack effective coordination. To bridge these gaps, we propose UTICN (Ubiquitous and Trusted Intelligent Cellular-native Network) for LAE, a unified cellular-native architecture that integrates multi-domain sensing, high-precision positioning, intelligent aircraft-to-everything communication, dynamic airspace management, and UAV operational services. UTICN introduces key technologies such as integrated sensing and communication (ISAC), passive and active positioning, intelligent machine communication, swarm coordination, and control-data decoupled management frameworks. We demonstrate UTICN's feasibility through two use cases, i.e., a city-level LAE management platform and a multi-frequency collaborative ISAC system. This work provides a fundamental reference for building a unified operational foundation and airspace management architecture for the LAE.
A Data-driven Predictive Control Architecture for Train Thermal Energy Management
Authors: Ahmed Aboudonia, Johannes Estermann, Keith Moffat, Manfred Morari, John Lygeros
Date: 2025-06-10 00:00:00
Tasks: energy management, Management

We aim to improve the energy efficiency of train climate control architectures, with a focus on a specific class of regional trains operating throughout Switzerland, especially in Zurich and Geneva. Heating, Ventilation, and Air Conditioning (HVAC) systems represent the second largest energy consumer in these trains after traction. The current architecture comprises a high-level rule-based controller and a low-level tracking controller. To improve train energy efficiency, we propose adding a middle data-driven predictive control layer aimed at minimizing HVAC energy consumption while maintaining passenger comfort. The scheme incorporates a multistep prediction model developed using real-world data collected from a limited number of train coaches. To validate the effectiveness of the proposed architecture, we conduct multiple experiments on a separate set of train coaches; our results suggest energy savings between 10% and 35% with respect to the current architecture.
Optimal Task Offloading with Firm Deadlines for Mobile Edge Computing Systems
Authors: Khai Doan, Wesley Araujo, Evangelos Kranakis, Ioannis Lambadaris, Yannis Viniotis, Wonjae Shin
Date: 2025-06-10 00:00:00
Tasks: Edge-computing

Under a dramatic increase in mobile data traffic, a promising solution for edge computing systems to maintain their local service is the task migration that may be implemented by means of Autonomous mobile agents (AMA). In designing an optimal scheme for task offloading to AMA, we define a system cost as a minimization objective function that comprises two parts. First, an offloading cost which can be interpreted as the cost of using computational resources from the AMA. Second, a penalty cost due to potential task expiration. To minimize the expected (timeaverage) cost over a given time horizon, we formulate a Dynamic programming (DP). However, the DP Equation suffers from the well-known curse of dimensionality, which makes computations intractable, especially for infinite system state space. To reduce the computational burden, we identify three important properties of the optimal policy and show that it suffices to evaluate the DP Equation on a finite subset of the state space only. We then prove that the optimal task offloading decision at a state can be inferred from that at its adjacent states, further reducing the computational load. We present simulations to verify the theoretical results and to provide insights into the considered system.