pwc-archive/papers-with-abstracts huggingface dataset:
Safety Filter for Limiting the Current of Grid-Forming Matrix Modular Multilevel Converters
Authors: Michael Schneeberger, Silvia Mastellone, Florian Dörfler
Date: 2025-03-13 00:00:00

Grid-forming (GFM) converters face significant challenges in limiting current during transient grid events while preserving their grid-forming behavior. This paper offers an elegant solution to the problem with a priori guarantees, presenting a safety filter approach based on Control Barrier Functions (CBFs) to enforce current constraints with minimal deviation from the nominal voltage reference. The safety filter is implemented as a Quadratic Program, enabling real-time computation of safe voltage adjustments that ensure smooth transitions and maintain the GFM behavior during nominal operation. To provide formal safety certificate, the CBF is synthesized offline using a Sum-of-Squares optimization framework, ensuring that the converter remains within its allowable operating limits under all conditions. Additionally, a Control Lyapunov Function is incorporated to facilitate a smooth return to the nominal operating region following grid events. The proposed method is modular and can be integrated into many of the GFM control architectures, as demonstrated with two different GFM implementations. High-fidelity simulations conducted with an enhanced matrix modular multilevel converter connected to both high-inertia and low-inertia grid scenarios validate the effectiveness of the safety filter, showing that it successfully limits current during faults, preserves GFM behavior, and ensures a seamless recovery to nominal operation.
Optimal Estimation for Continuous-Time Nonlinear Systems Using State-Dependent Riccati Equation (SDRE)
Authors: Adnan Tahirovic, Azra Redzovic
Date: 2025-03-13 00:00:00
Tasks: State Estimation

This paper introduces a unified approach for state estimation and control of nonlinear dynamic systems, employing the State-Dependent Riccati Equation (SDRE) framework. The proposed approach naturally extends classical linear quadratic Gaussian (LQG) methods into nonlinear scenarios, avoiding linearization by using state-dependent coefficient (SDC) matrices. An SDRE-based Kalman filter (SDRE-KF) is integrated within an SDRE-based control structure, providing a coherent and intuitive strategy for nonlinear system analysis and control design. To evaluate the effectiveness and robustness of the proposed methodology, comparative simulations are conducted on two benchmark nonlinear systems: a simple pendulum and a Van der Pol oscillator. Results demonstrate that the SDRE-KF achieves comparable or superior estimation accuracy compared to traditional methods, including the Extended Kalman Filter (EKF) and Particle Filter (PF). These findings underline the potential of the unified SDRE-based approach as a viable alternative for nonlinear state estimation and control, providing valuable insights for both educational purposes and practical engineering applications.
Safe exploration in reproducing kernel Hilbert spaces
Authors: Abdullah Tokmak, Kiran G. Krishnan, Thomas B. Schön, Dominik Baumann
Date: 2025-03-13 00:00:00
Tasks: Bayesian Optimization, Safe Exploration

Popular safe Bayesian optimization (BO) algorithms learn control policies for safety-critical systems in unknown environments. However, most algorithms make a smoothness assumption, which is encoded by a known bounded norm in a reproducing kernel Hilbert space (RKHS). The RKHS is a potentially infinite-dimensional space, and it remains unclear how to reliably obtain the RKHS norm of an unknown function. In this work, we propose a safe BO algorithm capable of estimating the RKHS norm from data. We provide statistical guarantees on the RKHS norm estimation, integrate the estimated RKHS norm into existing confidence intervals and show that we retain theoretical guarantees, and prove safety of the resulting safe BO algorithm. We apply our algorithm to safely optimize reinforcement learning policies on physics simulators and on a real inverted pendulum, demonstrating improved performance, safety, and scalability compared to the state-of-the-art.
Some remarks on robustness of sample-and-hold stabilization
Authors: Patrick Schmidt, Pavel Osinenko, Stefan Streif
Date: 2025-03-13 00:00:00

This work studies robustness to system disturbance and measurement noise of some popular general practical stabilization techniques, namely, Dini aiming, optimization-based stabilization and inf-convolution stabilization. Common to all these techniques is the explicit usage of a (general nonsmooth) control Lyapunov function, thus allowing to see them as a kind of generalization to the celebrated Sontag's formula. It turns out that certain details of the above described robustness properties have not yet received the attention in literature they deserved. We provide new remarks, formalized in mathematical propositions, on robustness of selected popular stabilization techniques along with an extensive statistical case study on a robot parking problem.
Reach-Avoid-Stay-Collision-Avoidance Negotiation Framework for Multi-Agent Systems via Spatiotemporal Tubes
Authors: Mohd. Faizuddin Faruqui, Ratnangshu Das, Ravi Kumar L, Pushpak Jagtap
Date: 2025-03-13 00:00:00
Tasks: Collision Avoidance, Drone navigation, Robot Navigation

This study presents a multi-agent negotiation-based framework to obtain collision-free paths while performing prescribed-time reach-avoid-stay (RAS) tasks for agents with unknown dynamics and bounded disturbance. By employing spatiotemporal tubes to generate time-varying state constraints, we ensure that all agents adhere to RAS specifications using synthesized controllers. To prevent inter-agent collisions, a negotiation mechanism is proposed where successful negotiations result in spatiotemporal tubes for each agent fulfilling desired tasks. This approach results in a completely distributed, approximation-free control law for each agent. The effectiveness of this mechanism was validated through simulations of multi-agent robot navigation and drone navigation tasks involving prescribed-time RAS specifications and collision avoidance.
Global synchronization of multi-agent systems with nonlinear interactions
Authors: Anthony Couthures, Vineeth S. Varma, Samson Lasaulce, Irinel-Constantin Morarescu
Date: 2025-03-13 00:00:00
Tasks: Quantization

The paper addresses the synchronization of multi-agent systems with continuous-time dynamics interacting through a very general class of monotonic continuous signal functions that covers estimation biases, approximation of discrete quantization, or state-dependent estimation. Our analysis reveals that, in the setup under consideration, synchronization equilibria are exactly the fixed points of the signal function. We also derive intuitive stability conditions based on whether the signal underestimates or overestimates the state of the agents around these fixed points. Moreover, we show that network topology plays a crucial role in asymptotic synchronization. These results provide interesting insights into the interplay between communication nonlinearity and network connectivity, paving the way for advanced coordination strategies in complex systems.
One-bit consensus of controllable linear multi-agent systems with communication noises
Authors: Ru An, Ying Wang, Yanlong Zhao, Ji-Feng Zhang
Date: 2025-03-13 00:00:00

This paper addresses the one-bit consensus of controllable linear multi-agent systems (MASs) with communication noises. A consensus algorithm consisting of a communication protocol and a consensus controller is designed. The communication protocol introduces a linear compression encoding function to achieve a one-bit data rate, thereby saving communication costs. The consensus controller with a stabilization term and a consensus term is proposed to ensure the consensus of a potentially unstable but controllable MAS. Specifically, in the consensus term, we adopt an estimation method to overcome the information loss caused by one-bit communications and a decay step to attenuate the effect of communication noise. Two combined Lyapunov functions are constructed to overcome the difficulty arising from the coupling of the control and estimation. By establishing similar iterative structures of these two functions, this paper shows that the MAS can achieve consensus in the mean square sense at the rate of the reciprocal of the iteration number under the case with a connected fixed topology. Moreover, the theoretical results are generalized to the case with jointly connected Markovian switching topologies by establishing a certain equivalence relationship between the Markovian switching topologies and a fixed topology. Two simulation examples are given to validate the algorithm.
Combining Cooperative Re-Routing with Intersection Coordination for Connected and Automated Vehicles in Urban Networks
Authors: Panagiotis Typaldos, Andreas A. Malikopoulos
Date: 2025-03-13 00:00:00
Tasks: Trajectory Planning

In this paper, we present a hierarchical framework that integrates upper-level routing with low-level optimal trajectory planning for connected and automated vehicles (CAVs) traveling in an urban network. The upper-level controller efficiently distributes traffic flows by utilizing a dynamic re-routing algorithm that leverages real-time density information and the fundamental diagrams of each network edge. This re-routing approach predicts when each edge will reach critical density and proactively adjusts the routing algorithm's weights to prevent congestion before it occurs. The low-level controller coordinates CAVs as they cross signal-free intersections, generating optimal, fuel-efficient trajectories while ensuring safe passage by satisfying all relevant constraints. We formulate the problem as an optimal control problem and derive an analytical solution. Using the SUMO micro-simulation platform, we conduct simulation experiments on a realistic network. The results show that our hierarchical framework significantly enhances network performance compared to a baseline static routing approach. By dynamically re-routing vehicles, our approach successfully reduces total travel time and mitigates congestion before it develops.
Growing Into an Adaptive and Reconfigurable Paradigm for Spectrum Sharing
Authors: Charles Baylis, Douglas Sicker, Austin Egbert, Andrew Clegg, Tom Brooks, Casey Latham, Robert J. Marks II
Date: 2025-03-13 00:00:00

A significant movement from rigid use of the wireless spectrum toward adaptive and reconfigurable spectrum use has been prompted by increasing spectral crowding. Some bands have moved to an adaptive sharing model, and proposals are growing for this approach to be applied to additional bands. The process of moving from a fixed, rigid spectrum paradigm to adaptive and reconfigurable use involves maturation of policy and technology at multiple levels within the system of systems. Using the concept of Bloom's Taxonomy from the education discipline, this paper examines the development of a policy and technology progression toward a mature, adaptive and reconfigurable paradigm.
Human Physical Interaction based on UAV Cooperative Payload Transportation System using Adaptive Backstepping and FNTSMC
Authors: Hussein N. Naser, Hashim A. Hashim, Mojtaba Ahmadi
Date: 2025-03-13 00:00:00
Tasks: Position

This paper presents a nonlinear control strategy for an aerial cooperative payload transportation system consisting of two quadrotor UAVs rigidly connected to a payload. The system includes human physical interaction facilitated by an admittance control. The proposed control framework integrates an adaptive Backstepping controller for the position subsystem and a Fast Nonsingular Terminal Sliding Mode Control (FNTSMC) for the attitude subsystem to ensure asymptotic stabilization. The admittance controller interprets the interaction forces from the human operator, generating reference trajectories for the position controller to ensure accurate tracking of the operator's guidance. The system aims to assist humans in payload transportation, providing both stability and responsiveness. The robustness and effectiveness of the proposed control scheme in maintaining system stability and performance under various conditions are presented.
Data-Driven Distributionally Robust Control for Interacting Agents under Logical Constraints
Authors: Arash Bahari Kordabad, Eleftherios E. Vlahakis, Lars Lindemann, Sebastien Gros, Dimos V. Dimarogonas, Sadegh Soudjani
Date: 2025-03-12 00:00:00

In this paper, we propose a distributionally robust control synthesis for an agent with stochastic dynamics that interacts with other agents under uncertainties and constraints expressed by signal temporal logic (STL). We formulate the control synthesis as a chance-constrained program (CCP) with STL specifications that must be satisfied with high probability under all uncertainty tubes induced by the other agents. To tackle the CCP, we propose two methods based on concentration of measure (CoM) theory and conditional value at risk (CVaR) and compare the required assumptions and resulting optimizations. These approaches convert the CCP into an expectation-constrained program (ECP), which is simpler to solve than the original CCP. To estimate the expectation using a finite set of observed data, we adopt a distributionally robust optimization (DRO) approach. The underlying DRO can be approximated as a robust data-driven optimization that provides a probabilistic under-approximation to the original ECP, where the probability depends on the number of samples. Therefore, under feasibility, the original STL constraints are satisfied with two layers of designed confidence: the confidence of the chance constraint and the confidence of the approximated data-driven optimization, which depends on the number of samples. We then provide details on solving the resulting robust data-driven optimization numerically. Finally, we compare the two proposed approaches through case studies.
Real-Time Risky Fault-Chain Search using Time-Varying Graph RNNs
Authors: Anmol Dwivedi, Ali Tajer
Date: 2025-03-12 00:00:00

This paper introduces a data-driven graphical framework for the real-time search of risky cascading fault chains (FCs) in power-grids, crucial for enhancing grid resiliency in the face of climate change. As extreme weather events driven by climate change increase, identifying risky FCs becomes crucial for mitigating cascading failures and ensuring grid stability. However, the complexity of the spatio-temporal dependencies among grid components and the exponential growth of the search space with system size pose significant challenges to modeling and risky FC search. To tackle this, we model the search process as a partially observable Markov decision process (POMDP), which is subsequently solved via a time-varying graph recurrent neural network (GRNN). This approach captures the spatial and temporal structure induced by the system's topology and dynamics, while efficiently summarizing the system's history in the GRNN's latent space, enabling scalable and effective identification of risky FCs.
The Pitfalls of Imitation Learning when Actions are Continuous
Authors: Max Simchowitz, Daniel Pfrommer, Ali Jadbabaie
Date: 2025-03-12 00:00:00
Tasks: Chunking, Imitation Learning, Offline RL

We study the problem of imitating an expert demonstrator in a discrete-time, continuous state-and-action control system. We show that, even if the dynamics satisfy a control-theoretic property called exponentially stability (i.e. the effects of perturbations decay exponentially quickly), and the expert is smooth and deterministic, any smooth, deterministic imitator policy necessarily suffers error on execution that is exponentially larger, as a function of problem horizon, than the error under the distribution of expert training data. Our negative result applies to any algorithm which learns solely from expert data, including both behavior cloning and offline-RL algorithms, unless the algorithm produces highly "improper" imitator policies--those which are non-smooth, non-Markovian, or which exhibit highly state-dependent stochasticity--or unless the expert trajectory distribution is sufficiently "spread." We provide experimental evidence of the benefits of these more complex policy parameterizations, explicating the benefits of today's popular policy parameterizations in robot learning (e.g. action-chunking and Diffusion Policies). We also establish a host of complementary negative and positive results for imitation in control systems.
Architecture-Aware Minimization (A$^2$M): How to Find Flat Minima in Neural Architecture Search
Authors: Matteo Gambella, Fabrizio Pittorino, Manuel Roveri
Date: 2025-03-13 00:00:00
Tasks: Neural Architecture Search

Neural Architecture Search (NAS) has become an essential tool for designing effective and efficient neural networks. In this paper, we investigate the geometric properties of neural architecture spaces commonly used in differentiable NAS methods, specifically NAS-Bench-201 and DARTS. By defining flatness metrics such as neighborhoods and loss barriers along paths in architecture space, we reveal locality and flatness characteristics analogous to the well-known properties of neural network loss landscapes in weight space. In particular, we find that highly accurate architectures cluster together in flat regions, while suboptimal architectures remain isolated, unveiling the detailed geometrical structure of the architecture search landscape. Building on these insights, we propose Architecture-Aware Minimization (A$^2$M), a novel analytically derived algorithmic framework that explicitly biases, for the first time, the gradient of differentiable NAS methods towards flat minima in architecture space. A$^2$M consistently improves generalization over state-of-the-art DARTS-based algorithms on benchmark datasets including CIFAR-10, CIFAR-100, and ImageNet16-120, across both NAS-Bench-201 and DARTS search spaces. Notably, A$^2$M is able to increase the test accuracy, on average across different differentiable NAS methods, by +3.60\% on CIFAR-10, +4.60\% on CIFAR-100, and +3.64\% on ImageNet16-120, demonstrating its superior effectiveness in practice. A$^2$M can be easily integrated into existing differentiable NAS frameworks, offering a versatile tool for future research and applications in automated machine learning. We open-source our code at https://github.com/AI-Tech-Research-Lab/AsquaredM.
Meta-learning characteristics and dynamics of quantum systems
Authors: Lucas Schorling, Pranav Vaidhyanathan, Jonas Schuff, Miguel J. Carballido, Dominik Zumbühl, Gerard Milburn, Florian Marquardt, Jakob Foerster, Michael A. Osborne, Natalia Ares
Date: 2025-03-13 00:00:00
Tasks: Computational Efficiency, Meta-Learning

While machine learning holds great promise for quantum technologies, most current methods focus on predicting or controlling a specific quantum system. Meta-learning approaches, however, can adapt to new systems for which little data is available, by leveraging knowledge obtained from previous data associated with similar systems. In this paper, we meta-learn dynamics and characteristics of closed and open two-level systems, as well as the Heisenberg model. Based on experimental data of a Loss-DiVincenzo spin-qubit hosted in a Ge/Si core/shell nanowire for different gate voltage configurations, we predict qubit characteristics i.e. $g$-factor and Rabi frequency using meta-learning. The algorithm we introduce improves upon previous state-of-the-art meta-learning methods for physics-based systems by introducing novel techniques such as adaptive learning rates and a global optimizer for improved robustness and increased computational efficiency. We benchmark our method against other meta-learning methods, a vanilla transformer, and a multilayer perceptron, and demonstrate improved performance.
Siamese Foundation Models for Crystal Structure Prediction
Authors: Liming Wu, Wenbing Huang, Rui Jiao, Jianxing Huang, Liwei Liu, Yipeng Zhou, Hao Sun, Yang Liu, Fuchun Sun, Yuxiang Ren, JiRong Wen
Date: 2025-03-13 00:00:00
Tasks: Prediction

Crystal Structure Prediction (CSP), which aims to generate stable crystal structures from compositions, represents a critical pathway for discovering novel materials. While structure prediction tasks in other domains, such as proteins, have seen remarkable progress, CSP remains a relatively underexplored area due to the more complex geometries inherent in crystal structures. In this paper, we propose Siamese foundation models specifically designed to address CSP. Our pretrain-finetune framework, named DAO, comprises two complementary foundation models: DAO-G for structure generation and DAO-P for energy prediction. Experiments on CSP benchmarks (MP-20 and MPTS-52) demonstrate that our DAO-G significantly surpasses state-of-the-art (SOTA) methods across all metrics. Extensive ablation studies further confirm that DAO-G excels in generating diverse polymorphic structures, and the dataset relaxation and energy guidance provided by DAO-P are essential for enhancing DAO-G's performance. When applied to three real-world superconductors ($\text{CsV}_3\text{Sb}_5$, $ \text{Zr}_{16}\text{Rh}_8\text{O}_4$ and $\text{Zr}_{16}\text{Pd}_8\text{O}_4$) that are known to be challenging to analyze, our foundation models achieve accurate critical temperature predictions and structure generations. For instance, on $\text{CsV}_3\text{Sb}_5$, DAO-G generates a structure close to the experimental one with an RMSE of 0.0085; DAO-P predicts the $T_c$ value with high accuracy (2.26 K vs. the ground-truth value of 2.30 K). In contrast, conventional DFT calculators like Quantum Espresso only successfully derive the structure of the first superconductor within an acceptable time, while the RMSE is nearly 8 times larger, and the computation speed is more than 1000 times slower. These compelling results collectively highlight the potential of our approach for advancing materials science research and development.