pwc-archive/papers-with-abstracts huggingface dataset:
Integrated Sensing, Communication, and Over-The-Air Control of UAV Swarm Dynamics
Authors: Zhuangkun Wei, Wenxiu Hu, Yathreb Bouazizi, Mengbang Zou, ChenGuang Liu, Yunfei Chen, Hongjian Sun, Julie McCann
Date: 2025-02-11 00:00:00
Tasks: ISAC

Coordinated controlling a large UAV swarm requires significant spectrum resources due to the need for bandwidth allocation per UAV, posing a challenge in resource-limited environments. Over-the-air (OTA) control has emerged as a spectrum-efficient approach, leveraging electromagnetic superposition to form control signals at a base station (BS). However, existing OTA controllers lack sufficient optimization variables to meet UAV swarm control objectives and fail to integrate control with other BS functions like sensing. This work proposes an integrated sensing and OTA control framework (ISAC-OTA) for UAV swarm. The BS performs OTA signal construction (uplink) and dispatch (downlink) while simultaneously sensing objects. Two uplink post-processing methods are developed: a control-centric approach generating closed-form control signals via a feedback-looped OTA control problem, and a sensing-centric method mitigating transmission-induced interference for accurate object sensing. For the downlink, a non-convex problem is formulated and solved to minimize control signal dispatch (transmission) error while maintaining a minimum sensing signal-to-noise ratio (SNR). Simulation results show that the proposed ISAC-OTA controller achieves control performance comparable to the benchmark optimal control algorithm while maintaining high sensing accuracy, despite OTA transmission interference. Moreover, it eliminates the need for per-UAV bandwidth allocation, showcasing a spectrum-efficient method for cooperative control in future wireless systems.
Mixed Integer Linear Programming for Active Contact Selection in Deep Brain Stimulation
Authors: Anna Franziska Frigge, Alexander Medvedev
Date: 2025-02-11 00:00:00
Tasks: Computational Efficiency

Deep brain stimulation (DBS) programming remains a complex and time-consuming process, requiring manual selection of stimulation parameters to achieve therapeutic effects while minimizing adverse side-effects. This study explores mathematical optimization for DBS programming, using functional subdivisions of the subthalamic nucleus (STN) to define the desired activation profile. A Mixed Integer Linear Programming (MILP) framework is presented allowing for dissimilar current distribution across active contacts. MILP is compared to a Linear Programming (LP) approach in terms of computational efficiency and activation accuracy. Results from ten Parkinson's disease patients treated with DBS show that while MILP better matches the predefined stimulation target activation profile, LP solutions more closely resemble clinically applied settings, suggesting the profile may not fully capture clinically relevant patterns. Additionally, MILP's limitations are discussed, including its reliance on precisely defined target regions and its computational burden for larger target sets.
Physics-Informed Recurrent Network for State-Space Modeling of Gas Pipeline Networks
Authors: Siyuan Wang, Wenchuan Wu, Chenhui Lin, Qi Wang, Shuwei Xu, Binbin Chen
Date: 2025-02-11 00:00:00
Tasks: State Space Models

As a part of the integrated energy system (IES), gas pipeline networks can provide additional flexibility to power systems through coordinated optimal dispatch. An accurate pipeline network model is critical for the optimal operation and control of IESs. However, inaccuracies or unavailability of accurate pipeline parameters often introduce errors in the state-space models of such networks. This paper proposes a physics-informed recurrent network (PIRN) to identify the state-space model of gas pipelines. It fuses sparse measurement data with fluid-dynamic behavior expressed by partial differential equations. By embedding the physical state-space model within the recurrent network, parameter identification becomes an end-to-end PIRN training task. The model can be realized in PyTorch through modifications to a standard RNN backbone. Case studies demonstrate that our proposed PIRN can accurately estimate gas pipeline models from sparse terminal node measurements, providing robust performance and significantly higher parameter efficiency. Furthermore, the identified state-space model of the pipeline network can be seamlessly integrated into optimization frameworks.
Non-Iterative Coordination of Interconnected Power Grids via Dimension-Decomposition-Based Flexibility Aggregation
Authors: Siyuan Wang, Cheng Feng, Fengqi You
Date: 2025-02-11 00:00:00

The bulk power grid is divided into regional grids interconnected with multiple tie-lines for efficient operation. Since interconnected power grids are operated by different control centers, it is a challenging task to realize coordinated dispatch of multiple regional grids. A viable solution is to compute a flexibility aggregation model for each regional power grid, then optimize the tie-line schedule using the aggregated models to implement non-iterative coordinated dispatch. However, challenges such as intricate interdependencies and curse of dimensionality persist in computing the aggregated models in high-dimensional space. Existing methods like Fourier-Motzkin elimination, vertex search, and multi-parameter programming are limited by dimensionality and conservatism, hindering practical application. This paper presents a novel dimension-decomposition-based flexibility aggregation algorithm for calculating the aggregated models of multiple regional power grids, enabling non-iterative coordination in large-scale interconnected systems. Compared to existing methods, the proposed approach yields a significantly less conservative flexibility region. The derived flexibility aggregation model for each regional power grid has a well-defined physical counterpart, which facilitates intuitive analysis of multi-port regional power grids and provides valuable insights into their internal resource endowments. Numerical tests validate the feasibility of the aggregated model and demonstrate its accuracy in coordinating interconnected power grids.
Morphing Wing Designs in Commercial Aviation
Authors: Chengyue Dong, Mansur M. Arief
Date: 2025-02-11 00:00:00

With increasing demands for fuel efficiency and operational adaptability in commercial aviation}, this paper provides a systematic review and classification of morphing wing technologies, analyzing their aerodynamic performance characteristics and atmospheric condition adaptability. We first develop a comprehensive classification framework for morphing wing designs based on their scale of morphing, actuation mechanisms, and intended purposes. Through analysis of historical developments and current implementations, we evaluate two significant case studies: the Mission Adaptive Compliant Wing (MACW) and Adaptive Aspect Ratio (AdAR) morphing wing, demonstrating performance improvements of up to 25% in drag reduction and 40% in control authority. Our investigation reveals critical trade-offs between full-span and partial morphing approaches, particularly regarding implementation complexity, certification requirements, and operational reliability. The study concludes with an assessment of technical barriers and opportunities, providing specific recommendations for advancing morphing wing technology in commercial aviation applications. Key findings indicate that while material science and control system advances enable practical implementation, certification pathways and maintenance considerations remain critical challenges for widespread adoption.
Optimal Steady-State Secondary Control of MT-HVdc Grids with Reduced Communications
Authors: Babak Abdolmaleki, Gilbert Bergna-Diaz
Date: 2025-02-10 00:00:00

In this paper, we propose a centralized secondary control for the real-time steady-state optimization of multi-terminal HVdc grids under voltage and current limits. First, we present the dynamic models of the grid components, including the modular multilevel converter (MMC) stations and their different control layers. We also derive the quasi-static input-output model of the system, which is suitable for the steady-state control design. Second, we formulate a general optimization problem using this quasi-static model and find the Karush-Kuhn-Tucker optimality conditions of its solutions. Third, we propose a secondary control based on primal-dual dynamics to adjust the voltage setpoints of the dispatchable MMCs, with which the system asymptotically converges to a steady state that satisfies these optimality conditions. Fourth, we provide a communication triggering mechanism to reduce the communication traffic between the secondary control unit and the MMC stations. Finally, we verify our proposal for different case studies by adapting it to an offshore multi-terminal HVdc grid composed of heterogeneous MMC stations simulated in the MATLAB/Simulink environment. The problems of proportional current minimization and loss reduction are two special case studies.
Real-time Optimization for Wind-to-H2 Driven Critical Infrastructures Based on Active Constraints Identification and Integer Variables Prediction
Authors: Mostafa Goodarzi, QiFeng Li
Date: 2025-02-10 00:00:00

This paper proposes a concept of wind-to-hydrogen-driven critical infrastructure (W2H-CI) as an engineering solution for decarbonizing the power generation sector where it utilizes wind power to produce hydrogen through electrolysis and combines it with the carbon captured from fossil fuel power plants. First, a convex mathematical model of W2H-CI is developed. Then, an optimization model for optimal operation of W2H-CI, which is a large-scale mixed-integer convex program (MICP), is proposed. Moreover, we propose to solve this problem in real-time in order to hedge against the uncertainty of wind power. For this purpose, a novel solution method based on active constraints identification and integer variable prediction is introduced. This method can solve MICP problems very fast since it uses historical optimization data to predict the values of binary variables and a limited number of constraints which most likely contain all active constraints. We validate the effectiveness of the proposed fast solution method using two W2H-CI case studies.
Comprehensive Analysis of Thermal Dissipation in Lithium-Ion Battery Packs
Authors: Xuguang Zhang, Hexiang Zhang, Amjad Almansour, Mrityunjay Singh, Hengling Zhu, Michael C. Halbig, Yi Zheng
Date: 2025-02-10 00:00:00
Tasks: Management

Effective thermal management is critical for lithium-ion battery packs' safe and efficient operations, particularly in applications such as drones, where compact designs and varying airflow conditions present unique challenges. This study investigates the thermal performance of a 16-cell lithium-ion battery pack by optimizing cooling airflow configurations and integrating phase change materials (PCMs) for enhanced heat dissipation. Seven geometric configurations were evaluated under airflow speeds ranging from 0 to 15 m/s, reflecting the operational conditions of civilian drones. A comprehensive 3D simulation approach was used to analyze the effects of inlet and outlet configurations, airflow dynamics, and PCM phase transition behavior. Results indicate that the trapezoidal (wide-base) configuration, paired with a 5-inlet and 1-outlet setup, achieves the most balanced performance, effectively maintaining optimal operating temperatures across low and high-speed airflow conditions. PCM integration further stabilized thermal behavior, with phase change durations extending to 12.5 min under tested conditions. These findings highlight the importance of geometric optimization and material integration in advancing compact and reliable thermal management systems for energy-dense battery packs. This study provides a foundation for designing efficient cooling strategies tailored to lightweight applications such as drones and portable energy storage systems.
Active Inference through Incentive Design in Markov Decision Processes
Authors: Xinyi Wei, Chongyang Shi, Shuo Han, Ahmed H. Hemida, Charles A. Kamhoua, Jie Fu
Date: 2025-02-10 00:00:00

We present a method for active inference with partial observations in stochastic systems through incentive design, also known as the leader-follower game. Consider a leader agent who aims to infer a follower agent's type given a finite set of possible types. Different types of followers differ in either the dynamical model, the reward function, or both. We assume the leader can partially observe a follower's behavior in the stochastic system modeled as a Markov decision process, in which the follower takes an optimal policy to maximize a total reward. To improve inference accuracy and efficiency, the leader can offer side payments (incentives) to the followers such that different types of them, under the incentive design, can exhibit diverging behaviors that facilitate the leader's inference task. We show the problem of active inference through incentive design can be formulated as a special class of leader-follower games, where the leader's objective is to balance the information gain and cost of incentive design. The information gain is measured by the entropy of the estimated follower's type given partial observations. Furthermore, we demonstrate that this problem can be solved by reducing a single-level optimization through softmax temporal consistency between followers' policies and value functions. This reduction allows us to develop an efficient gradient-based algorithm. We utilize observable operators in the hidden Markov model (HMM) to compute the necessary gradients and demonstrate the effectiveness of our approach through experiments in stochastic grid world environments.
Solving Optimal Power Flow on a Data-Budget: Feature Selection on Smart Meter Data
Authors: Vassilis Kekatos, Ridley Annin, Manish K. Singh, Junjie Qin
Date: 2025-02-10 00:00:00
Tasks: Data Compression, feature selection

How much data is needed to optimally schedule distributed energy resources (DERs)? Does the distribution system operator (DSO) have to precisely know load demands and solar injections at each bus of the feeder to solve an optimal power flow (OPF)? This work exploits redundancies in OPF's structure and data to avoid communicating such data deluge, and explores the trade-off between data compression and grid's performance. We propose an OPF data distillation framework involving two steps. The DSO first collects OPF data from only a subset of nodes. The DSO subsequently reconstructs the complete OPF data from the partial ones, and feeds them into the OPF solver. Selecting and reconstructing OPF data may be performed to either maximize the fidelity of reconstructed OPF data, or maximize the fidelity of OPF solutions corresponding to reconstructed data. Under the first objective, OPF data distillation is posed as a sparsity-regularized convex problem. Under the second objective, it is posed as a sparsity-regularized bilevel program. Both problems are solved using accelerated proximal gradient (PGD) algorithms. Numerical tests corroborate that the bilevel formulation enhances fidelity and feasibility of reconstructed OPF solutions, and that OPF solutions can be approximated reasonably well even from partial data.
Koopman-Equivariant Gaussian Processes
Authors: Petar Bevanda, Max Beier, Armin Lederer, Alexandre Capone, Stefan Sosnowski, Sandra Hirche
Date: 2025-02-10 00:00:00
Tasks: Decision Making, Gaussian Processes, Representation Learning, Variational Inference

Credible forecasting and representation learning of dynamical systems are of ever-increasing importance for reliable decision-making. To that end, we propose a family of Gaussian processes (GP) for dynamical systems with linear time-invariant responses, which are nonlinear only in initial conditions. This linearity allows us to tractably quantify forecasting and representational uncertainty, simultaneously alleviating the challenge of computing the distribution of trajectories from a GP-based dynamical system and enabling a new probabilistic treatment of learning Koopman operator representations. Using a trajectory-based equivariance -- which we refer to as \textit{Koopman equivariance} -- we obtain a GP model with enhanced generalization capabilities. To allow for large-scale regression, we equip our framework with variational inference based on suitable inducing points. Experiments demonstrate on-par and often better forecasting performance compared to kernel-based methods for learning dynamical systems.
Towards Closing the Gap between Model-Based Systems Engineering and Automated Vehicle Assurance: Tailoring Generic Methods by Integrating Domain Knowledge
Authors: Marcus Nolte, Markus Maurer
Date: 2025-02-10 00:00:00

Designing, assuring and releasing safe automated vehicles is a highly interdisciplinary process. As complex systems, automated driving systems will inevitably be subject to emergent properties, i. e., the properties of the overall system will be more than just a sum of the properties of its integrated elements. Safety is one example of such emergent properties. In this regard, it must be ensured that effects of emergence do not render an overall system that is composed of safety-approved sub systems unsafe. The key challenges in this regard are twofold: Regarding the interdisciplinary character of the development and assurance processes, all relevant stakeholders must speak a common language and have a common understanding of the key concepts that influence system safety. Additionally, the individual properties of system elements should remain traceable to the system level. Model-Based Systems Engineering (MBSE) provides an interdisciplinary mindset, as well as methods and processes to manage emergent system properties over the entire system lifecycle. By this, MBSE provides tools that can assist the assurance process for automated vehicles. However, concepts from the domain of MBSE have a reputation for not being directly accessible for domain experts who are no experts in the field of Systems Engineering. This paper highlights challenges when applying MBSE methods to the design and development of automated driving systems. It will present an approach to create and apply domain-specific SysML profiles, which can be a first step for enhancing communication between different stake-holders in the development and safety assurance processes.
A Review of Conceptualizations of Safety and Risk in Current Automated Driving Regulation
Authors: Marcus Nolte, Leon Johann Brettin, Hans Steege, Nayel Salem, Marvin Loba, Robert Graubohm, Markus Maurer
Date: 2025-02-10 00:00:00

"Safety" and "Risk" are key concepts for the design and development of automated vehicles. For the market introduction or large-scale field tests, both concepts are not only relevant for engineers developing the vehicles, but for all stakeholders (e.g., regulators, lawyers, or the general public) who have stakes in the technology. In the communication between stakeholder groups, common notions of these abstract concepts are key for efficient communication and setting mutual expectations. In the European market, automated vehicles require Europe-wide type approval or at least operating permits in the individual states. For this, a central means of communication between regulators and engineers are regulatory documents. Flawed terminology regarding the safety expectations for automated vehicles can unnecessarily complicate relations between regulators and manufacturers, and thus hinder the introduction of the technology. In this paper, we review relevant documents at the UN- and EU-level, for the UK, and Germany regarding their (implied) notions of safety and risk. We contrast the regulatory notions with established and more recently developing notions of safety and risk in the field of automated driving. Based on the analysis, we provide recommendations on how explicit definitions of safety and risk in regulatory documents can support rather than hinder the market introduction of automated vehicles.
Inventory Consensus Control in Supply Chain Networks using Dissipativity-Based Control and Topology Co-Design
Authors: Shirantha Welikala, Hai Lin, Panos J. Antsaklis
Date: 2025-02-10 00:00:00
Tasks: Computational Efficiency

Recent global and local phenomena have exposed vulnerabilities in critical supply chain networks (SCNs), drawing significant attention from researchers across various fields. Typically, SCNs are viewed as static entities regularly optimized to maintain their optimal operation. However, the dynamic nature of SCNs and their associated uncertainties have motivated researchers to treat SCNs as dynamic networked systems requiring robust control techniques. In this paper, we address the SCN inventory consensus problem, which aims to synchronize multiple parallel supply chains, enhancing coordination and robustness of the overall SCN. To achieve this, we take a novel approach exploiting dissipativity theory. In particular, we propose a dissipativity-based co-design strategy for distributed consensus controllers and communication topology in SCNs. It requires only the dissipativity information of the individual supply chains and involves solving a set of convex optimization problems, thus contributing to scalability, compositionality, and computational efficiency. Moreover, it optimizes the robustness of the SCN to various associated uncertainties, mitigating both bullwhip and ripple effects. We demonstrate our contributions using numerical examples, mainly by comparing the consensus performance with respect to standard steady-state control, feedback control, and consensus control strategies.
Stochastic MPC with Online-optimized Policies and Closed-loop Guarantees
Authors: Marcell Bartos, Alexandre Didier, Jerome Sieber, Johannes Köhler, Melanie N. Zeilinger
Date: 2025-02-10 00:00:00
Tasks: Model Predictive Control

This paper proposes a stochastic model predictive control method for linear systems affected by additive Gaussian disturbances. Closed-loop satisfaction of probabilistic constraints and recursive feasibility of the underlying convex optimization problem is guaranteed. Optimization over feedback policies online increases performance and reduces conservatism compared to fixed-feedback approaches. The central mechanism is a finitely determined maximal admissible set for probabilistic constraints, together with the reconditioning of the predicted probabilistic constraints on the current knowledge at every time step. The proposed method's reduced conservatism and improved performance in terms of the achieved closed-loop cost is demonstrated in a numerical example.
Toolbox for Developing Physics Informed Neural Networks for Power Systems Components
Authors: Ioannis Karampinis, Petros Ellinas, Ignasi Ventura Nadal, Rahul Nellikkath, Spyros Chatzivasileiadis
Date: 2025-02-10 00:00:00

This paper puts forward the vision of creating a library of neural-network-based models for power system simulations. Traditional numerical solvers struggle with the growing complexity of modern power systems, necessitating faster and more scalable alternatives. Physics-Informed Neural Networks (PINNs) offer promise to solve fast the ordinary differential equations (ODEs) governing power system dynamics. This is vital for the reliability, cost optimization, and real-time decision-making in the electricity grid. Despite their potential, standardized frameworks to train PINNs remain scarce. This poses a barrier for the broader adoption and reproducibility of PINNs; it also does not allow the streamlined creation of a PINN-based model library. This paper addresses these gaps. It introduces a Python-based toolbox for developing PINNs tailored to power system components, available on GitHub https://github. com/radiakos/PowerPINN. Using this framework, we capture the dynamic characteristics of a 9th-order system, which is probably the most complex power system component trained with a PINN to date, demonstrating the toolbox capabilities, limitations, and potential improvements. The toolbox is open and free to use by anyone interested in creating PINN-based models for power system components.