pwc-archive/papers-with-abstracts huggingface dataset:
Physical spline for denoising object trajectory data by combining splines, ML feature regression and model knowledge
Authors: Jonas Torzewski
Date: 2025-04-08 00:00:00
Tasks: Denoising, Position

This article presents a method for estimating the dynamic driving states (position, velocity, acceleration and heading) from noisy measurement data. The proposed approach is effective with both complete and partial observations, producing refined trajectory signals with kinematic consistency, ensuring that velocity is the integral of acceleration and position is the integral of velocity. Additionally, the method accounts for the constraint that vehicles can only move in the direction of their orientation. The method is implemented as a configurable python library that also enables trajectory estimation solely based on position data. Regularization is applied to prevent extreme state variations. A key application is enhancing recorded trajectory data for use as reference inputs in machine learning models. At the end, the article presents the results of the method along with a comparison to ground truth data.
Leveraging Non-Steady-State Frequency-Domain Data in Willems' Fundamental Lemma
Authors: T. J. Meijer, M. Wind, V. S. Dolk, W. P. M. H. Heemels
Date: 2025-04-08 00:00:00
Tasks: LEMMA

Willems' fundamental lemma enables data-driven analysis and control by characterizing an unknown system's behavior directly in terms of measured data. In this work, we extend a recent frequency-domain variant of this result--previously limited to steady-state data--to incorporate non-steady-state data including transient phenomena. This approach eliminates the need to wait for transients to decay during data collection, significantly reducing the experiment duration. Unlike existing frequency-domain system identification methods, our approach integrates transient data without preprocessing, making it well-suited for direct data-driven analysis and control. We demonstrate its effectiveness by isolating transients in the collected data and performing FRF evaluation at arbitrary frequencies in a numerical case study including noise.
A Scalable Automatic Model Generation Tool for Cyber-Physical Network Topologies and Data Flows for Large-Scale Synthetic Power Grid Models
Authors: Samantha Israel, Sanjana Kunkolienkar, Ana Goulart, Kate Davis, Thomas Overbye
Date: 2025-04-08 00:00:00

Power grids and their cyber infrastructure are classified as Critical Energy Infrastructure/Information (CEII) and are not publicly accessible. While realistic synthetic test cases for power systems have been developed in recent years, they often lack corresponding cyber network models. This work extends synthetic grid models by incorporating cyber-physical representations. To address the growing need for realistic and scalable models that integrate both cyber and physical layers in electric power systems, this paper presents the Scalable Automatic Model Generation Tool (SAM-GT). This tool enables the creation of large-scale cyber-physical topologies for power system models. The resulting cyber-physical network models include power system switches, routers, and firewalls while accounting for data flows and industrial communication protocols. Case studies demonstrate the tool's application to synthetic grid models of 500, 2,000, and 10,000 buses, considering three distinct network topologies. Results from these case studies include network metrics on critical nodes, hops, and generation times, showcasing effectiveness, adaptability, and scalability of SAM-GT.
Review, Definition and Challenges of Electrical Energy Hubs
Authors: Giacomo Bastianel, Jan Kircheis, Merijn Van Deyck, Dongyeong Lee, Geraint Chaffey, Marta Vanin, Hakan Ergun, Jef Beerten, Dirk Van Hertem
Date: 2025-04-08 00:00:00

To transition towards a carbon-neutral power system, considerable amounts of renewable energy generation capacity are being installed in the North Sea area. Consequently, projects aggregating many gigawatts of power generation capacity and transmitting renewable energy to the main load centers are being developed. Given the electrical challenges arising from having bulk power capacity in a compact geographical area with several connections to the main grid, and a lack of a robust definition identifying the type of system under study, this paper proposes a general technical definition of such projects introducing the term Electrical Energy Hub (EEH). The concept, purpose, and functionalities of EEHs are introduced in the text, emphasizing the importance of a clear technical definition for future planning procedures, grid codes, regulations, and support schemes for EEHs and multiterminal HVDC (MTDC) grids in general. Furthermore, the unique electrical challenges associated with integrating EEHs into the power system are discussed. Three research areas of concern are identified, namely control, planning, and protection. Through this analysis, insights are provided into the effective implementation of multi-GW scale EEH projects and their integration into the power grid through multiple interconnections. Finally, a list of ongoing and planned grid development projects is evaluated to assess whether they fall within the EEH category
A Metropolis-Adjusted Langevin Algorithm for Sampling Jeffreys Prior
Authors: Yibo Shi, Braghadeesh Lakshminarayanan, Cristian R. Rojas
Date: 2025-04-08 00:00:00

Inference and estimation are fundamental aspects of statistics, system identification and machine learning. For most inference problems, prior knowledge is available on the system to be modeled, and Bayesian analysis is a natural framework to impose such prior information in the form of a prior distribution. However, in many situations, coming out with a fully specified prior distribution is not easy, as prior knowledge might be too vague, so practitioners prefer to use a prior distribution that is as `ignorant' or `uninformative' as possible, in the sense of not imposing subjective beliefs, while still supporting reliable statistical analysis. Jeffreys prior is an appealing uninformative prior because it offers two important benefits: (i) it is invariant under any re-parameterization of the model, (ii) it encodes the intrinsic geometric structure of the parameter space through the Fisher information matrix, which in turn enhances the diversity of parameter samples. Despite these benefits, drawing samples from Jeffreys prior is a challenging task. In this paper, we propose a general sampling scheme using the Metropolis-Adjusted Langevin Algorithm that enables sampling of parameter values from Jeffreys prior, and provide numerical illustrations of our approach through several examples.
Restoring Feasibility in Power Grid Optimization: A Counterfactual ML Approach
Authors: Mostafa Mohammadian, Anna Van Boven, Kyri Baker
Date: 2025-04-08 00:00:00
Tasks: counterfactual, Decision Making

Electric power grids are essential components of modern life, delivering reliable power to end-users while adhering to a multitude of engineering constraints and requirements. In grid operations, the Optimal Power Flow problem plays a key role in determining cost-effective generator dispatch that satisfies load demands and operational limits. However, due to stressed operating conditions, volatile demand profiles, and increased generation from intermittent energy sources, this optimization problem may become infeasible, posing risks such as voltage instability and line overloads. This study proposes a learning framework that combines machine learning with counterfactual explanations to automatically diagnose and restore feasibility in the OPF problem. Our method provides transparent and actionable insights by methodically identifying infeasible conditions and suggesting minimal demand response actions. We evaluate the proposed approach on IEEE 30-bus and 300-bus systems, demonstrating its capability to recover feasibility with high success rates and generating diverse corrective options, appropriate for real-time decision-making. These preliminary findings illustrate the potential of combining classical optimization with explainable AI techniques to enhance grid reliability and resilience.
Plug and Play Distributed Control of Clustered Energy Hub Networks
Authors: Varsha Behrunani, Cara Koepele, Jared Miller, Ahmed Aboudonia, Philipp Heer, Roy S. Smith, John Lygeros
Date: 2025-04-08 00:00:00
Tasks: energy management, energy trading, Model Predictive Control

The transition to renewable energy is driving the rise of distributed multi-energy systems, in which individual energy hubs and prosumers (e.g., homes, industrial campuses) generate, store, and trade energy. Economic Model Predictive Control (MPC) schemes are widely used to optimize operation of energy hubs by efficiently dispatching resources and minimizing costs while ensuring operational constraints are met. Peer-to-peer (P2P) energy trading among hubs enhances network efficiency and reduces costs but also increases computational and privacy challenges, especially as the network scales. Additionally, current distributed control techniques require global recomputation whenever the network topology changes, limiting scalability. To address these challenges, we propose a clustering-based P2P trading framework that enables plug-and-play operation, allowing energy hubs to seamlessly join or leave without requiring network-wide controller updates. The impact is restricted to the hubs within the affected cluster. The energy trading problem is formulated as a bi-level bargaining game, where inter-cluster trading commitments are determined at the cluster level, while energy dispatch and cost-sharing among hubs within a cluster are refined at the hub level. Both levels are solved in a distributed manner using ADMM, ensuring computational feasibility and privacy preservation. Moreover, we develop plug-and-play procedures to handle dynamic topology changes at both the hub and cluster levels, minimizing disruptions across the network. Simulation results demonstrate that the proposed bi-level framework reduces operational costs, and enables scalable energy management under plug-and-play operation.
Linear Regulator-Based Synchronization of Positive Multi-Agent Systems
Authors: Alba Gurpegui, Mark Jeeninga, Emma Tegling, Anders Rantzer
Date: 2025-04-08 00:00:00

This paper addresses the positive synchronization of interconnected systems on undirected graphs. For homogeneous positive systems, a static feedback protocol design is proposed, based on the Linear Regulator problem. The solution to the algebraic equation associated to the stabilizing policy can be found using a linear program. Necessary and sufficient conditions on the positivity of each agent's trajectory for all nonnegative initial conditions are also provided. Simulations on large regular graphs with different nodal degree illustrate the proposed results.
Rhythmic neuromorphic control of a pendulum: A hybrid systems analysis
Authors: E. Petri, R. Postoyan, W. P. M. H. Heemels
Date: 2025-04-08 00:00:00
Tasks: Position

Neuromorphic engineering is an emerging research domain that aims to realize important implementation advantages that brain-inspired technologies can offer over classical digital technologies, including energy efficiency, adaptability, and robustness. For the field of systems and control, neuromorphic controllers could potentially bring many benefits, but their advancement is hampered by lack of systematic analysis and design tools. In this paper, the objective is to show that hybrid systems methods can aid in filling this gap. We do this by formally analyzing rhythmic neuromorphic control of a pendulum system, which was recently proposed as a prototypical setup. The neuromorphic controller generates spikes, which we model as a Dirac delta pulse, whenever the pendulum angular position crosses its resting position, with the goal of inducing a stable limit cycle. This leads to modeling the closed-loop system as a hybrid dynamical system, which in between spikes evolves in open loop and where the jumps correspond to the spiking control actions. Exploiting the hybrid system model, we formally prove the existence, uniqueness, and a stability property of the hybrid limit cycle for the closed-loop system. Numerical simulations illustrate our approach. We finally elaborate on a possible spiking adaptation mechanism on the pulse amplitude to generate a hybrid limit cycle of a desired maximal angular amplitude.
Smart Exploration in Reinforcement Learning using Bounded Uncertainty Models
Authors: J. S. van Hulst, W. P. M. H. Heemels, D. J. Antunes
Date: 2025-04-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) is a powerful tool for decision-making in uncertain environments, but it often requires large amounts of data to learn an optimal policy. We propose using prior model knowledge to guide the exploration process to speed up this learning process. This model knowledge comes in the form of a model set to which the true transition kernel and reward function belong. We optimize over this model set to obtain upper and lower bounds on the Q-function, which are then used to guide the exploration of the agent. We provide theoretical guarantees on the convergence of the Q-function to the optimal Q-function under the proposed class of exploring policies. Furthermore, we also introduce a data-driven regularized version of the model set optimization problem that ensures the convergence of the class of exploring policies to the optimal policy. Lastly, we show that when the model set has a specific structure, namely the bounded-parameter MDP (BMDP) framework, the regularized model set optimization problem becomes convex and simple to implement. In this setting, we also show that we obtain finite-time convergence to the optimal policy under additional assumptions. We demonstrate the effectiveness of the proposed exploration strategy in a simulation study. The results indicate that the proposed method can significantly speed up the learning process in reinforcement learning.
Hybrid Control as a Proxy for Detection and Mitigation of Sensor Attacks in Cooperative Driving
Authors: Mischa Huisman, Carlos Murguia, Erjen Lefeber, Nathan van de Wouw
Date: 2025-04-08 00:00:00

To enhance the robustness of cooperative driving against cyberattacks, we propose a hybrid controller scheme to detect and mitigate False-Data Injection (FDI) attacks in real-time. The core of our method builds on a given Cooperative Adaptive Cruise Control (CACC) algorithm and exploits sensor redundancy to construct equivalent controllers, each driven by a distinct, non-overlapping subset of sensors (equivalent controller realizations). By construction, these controller realizations generate the same control input in the absence of an attack, allowing us to devise an algorithm that compares control signals and measurements to pinpoint anomalous behavior via a majority vote. This allows us to: 1) decide in real-time which subset of sensors is compromised; and 2) switch to a healthy subset, mitigating thus sensor FDI attacks. We model the latter logic as a hybrid dynamic controller that decides in real-time what realization to use, builds on attack-dependent flow and jump sets, and employs controller resets (to return the state of previously compromised controller realizations to a correct value after the attack stops). We demonstrate the performance of our scheme through simulation experiments.
Control-Oriented Modelling and Adaptive Parameter Estimation for Hybrid Wind-Wave Energy Systems
Authors: Yingbo Huang, Bozhong Yuan, Haoran He, Jing Na, Yu Feng, Guang Li, Jing Zhao, Pak Kin Wong, Lin Cui
Date: 2025-04-08 00:00:00
Tasks: parameter estimation

Hybrid wind-wave energy system, integrating floating offshore wind turbine and wave energy converters, has received much attention in recent years due to its potential benefit in increasing the power harvest density and reducing the levelized cost of electricity. Apart from the design complexities of the hybrid wind-wave energy systems, their energy conversion efficiency, power output smoothness and their safe operations introduce new challenges for their control system designs. Recent studies show that advanced model-based control strategies have the great potential to significantly improve their overall control performance. However the performance of these advanced control strategies rely on the computationally efficient control-oriented models with sufficient fidelity, which are normally difficult to derive due to the complexity of the hydro-, aero-dynamic effects and the couplings.In most available results, the hybrid wind-wave energy system models are established by using the Boundary Element Method, devoting to understanding the hydrodynamic responses and performance analysis. However, such models are complex and involved relatively heavy computational burden, which cannot be directly used for the advanced model-based control methods that are essential for improving power capture efficiency from implementing in practice. To overcome this issue, this paper proposes a control-oriented model of the hybrid windwave energy system with six degrees of freedom. First, ...
InstructMPC: A Human-LLM-in-the-Loop Framework for Context-Aware Control
Authors: Ruixiang Wu, Jiahao Ai, Tongxin Li
Date: 2025-04-08 00:00:00
Tasks: energy management, Language Modeling, Language Modelling, Large Language Model, Model Predictive Control

Model Predictive Control (MPC) is a powerful control strategy widely utilized in domains like energy management, building control, and autonomous systems. However, its effectiveness in real-world settings is challenged by the need to incorporate context-specific predictions and expert instructions, which traditional MPC often neglects. We propose InstructMPC, a novel framework that addresses this gap by integrating real-time human instructions through a Large Language Model (LLM) to produce context-aware predictions for MPC. Our method employs a Language-to-Distribution (L2D) module to translate contextual information into predictive disturbance trajectories, which are then incorporated into the MPC optimization. Unlike existing context-aware and language-based MPC models, InstructMPC enables dynamic human-LLM interaction and fine-tunes the L2D module in a closed loop with theoretical performance guarantees, achieving a regret bound of $O(\sqrt{T\log T})$ for linear dynamics when optimized via advanced fine-tuning methods such as Direct Preference Optimization (DPO) using a tailored loss function.
Adaptive Extended Kalman Filtering for Battery State of Charge Estimation on STM32
Authors: António Barros, Edoardo Peretti, Davide Fabroni, Diego Carrera, Pasqualina Fragneto, Giacomo Boracchi
Date: 2025-04-08 00:00:00
Tasks: Management

Accurate and computationally light algorithms for estimating the State of Charge (SoC) of a battery's cells are crucial for effective battery management on embedded systems. In this letter, we propose an Adaptive Extended Kalman Filter (AEKF) for SoC estimation using a covariance adaptation technique based on maximum likelihood estimation - a novelty in this domain. Furthermore, we tune a key design parameter - the window size - to obtain an optimal memory-performance trade-off, and experimentally demonstrate our solution achieves superior estimation accuracy with respect to existing alternative methods. Finally, we present a fully custom implementation of the AEKF for a general-purpose low-cost STM32 microcontroller, showing it can be deployed with minimal computational requirements adequate for real-world usage.
A Douglas-Rachford Splitting Method for Solving Monotone Variational Inequalities in Linear-quadratic Dynamic Games
Authors: Reza Rahimi Baghbadorani, Emilio Benenati, Sergio Grammatico
Date: 2025-04-08 00:00:00

This paper considers constrained linear dynamic games with quadratic objective functions, which can be cast as affine variational inequalities. By leveraging the problem structure, we apply the Douglas-Rachford splitting, which generates a solution algorithm with linear convergence rate. The fast convergence of the method enables receding-horizon control architectures. Furthermore, we demonstrate that the associated VI admits a closed-form solution within a neighborhood of the attractor, thus allowing for a further reduction in computation time. Finally, we benchmark the proposed method via numerical experiments in an automated driving application.
Prescribed-Time Boresight Control of Spacecraft Under Pointing Constraints
Authors: Xiaodong Shao, Haoyang Yang, Haoran Li, Zongyu Zuo, Jose Guadalupe Romero, Qinglei Hu
Date: 2025-04-06 00:00:00

This article proposes an integrated boresight guidance and control (IBGC) scheme to address the boresight reorientation problem of spacecraft under temporal and pointing constraints. A $C^1$ continuous, saturated prescribed-time adjustment (PPTA) function is presented, along with the establishment of a practical prescribed-time stability criterion. Utilizing the time scale transformation technique and the PPTA function, we propose a prescribed-time guidance law that guides the boresight vector from almost any initial orientation in free space to a small neighborhood of the goal orientation within a preassigned time, while avoiding all forbidden zones augmented with safety margins. Subsequently, a prescribed-time disturbance observer (PTDO) is derived to reconstruct the external disturbances. By leveraging barrier and PPTA functions, a PTDO-based reduced-attitude tracking controller is developed, which ensures prescribed-time boresight tracking within a ``safe tube''. By judiciously setting the safety margins, settling times, and safe tube for the guidance and control laws, the proposed IBGC scheme achieves pointing-constrained boresight reorientation within a required task completion time. Simulation and experimental results demonstrate the efficacy of the proposed IBGC scheme.
Covariance-Intersection-based Distributed Kalman Filtering: Stability Problems Revisited
Authors: Zhongyao Hu, Bo Chen, Chao Sun, Li Yu
Date: 2025-04-08 00:00:00

This paper studies the stability of covariance-intersection (CI)-based distributed Kalman filtering in time-varying systems. For the general time-varying case, a relationship between the error covariance and the observability Gramian is established. Utilizing this relationship, we demonstrate an intuition that the stability of a node is only related to the observability of those nodes that can reach it uniformly. For the periodic time-varying case, it is proved by a monotonicity analysis method that CI-based distributed Kalman filtering converges periodically for any initial condition. The convergent point is shown to be the unique positive definite solution to a Riccati-like equation. Additionally, by constructing an intermediate difference equation, the closed-loop transition matrix of the estimation error system is proved to be Schur stable. Notably, all theoretical results are obtained without requiring network connectivity assumptions. Finally, simulations verify the effectiveness of the stability results.