pwc-archive/papers-with-abstracts huggingface dataset:
Distributed RISE-based Control for Exponential Heterogeneous Multi-Agent Target Tracking of Second-Order Nonlinear Systems
Authors: Cristian F. Nino, Omkar Sudhir Patil, Sage C. Edwards, Warren E. Dixon
Date: 2025-03-18 00:00:00

A distributed implementation of a Robust Integral of the Sign of the Error (RISE) controller is developed for multi-agent target tracking problems with exponential convergence guarantees. Previous RISE-based approaches for multi-agent systems required 2-hop communication, limiting practical applicability. New insights from a Lyapunov-based design-analysis approach are used to eliminate the need for multi-hop communication required in previous literature, while yielding exponential target tracking. The new insights include the development of a new P-function that works in tandem with the graph interaction matrix in the Lyapunov function. Nonsmooth Lyapunov-based stability analysis methods are used to yield semi-global exponential convergence to the target agent state despite the presence of bounded disturbances with bounded derivatives. The resulting outcome is a controller that achieves exponential target tracking with only local information exchange between neighboring agents.
Inference and Learning of Nonlinear LFR State-Space Models
Authors: Merijn Floren, Jean-Philippe Noël, Jan Swevers
Date: 2025-03-18 00:00:00
Tasks: State Space Models

Estimating the parameters of nonlinear block-oriented state-space models from input-output data typically involves solving a highly non-convex optimization problem, which is prone to poor local minima and slow convergence. This paper presents a computationally efficient initialization method for nonlinear linear fractional representation (NL-LFR) models using periodic data. By first inferring the latent signals and subsequently estimating the model parameters, the approach generates initial estimates for use in a later nonlinear optimization step. The proposed method shows robustness against poor local minima, and achieves a twofold error reduction compared to the state-of-the-art on a challenging benchmark dataset.
On the Standard Performance Criteria for Applied Control Design: PID, MPC or Machine Learning Controller?
Authors: Pouria Sarhadi
Date: 2025-03-18 00:00:00

The traditional control theory and its application to basic and complex systems have reached an advanced level of maturity. This includes aerial, marine, and ground vehicles, as well as robotics, chemical, transportation, and electrical systems widely used in our daily lives. The emerging era of data-driven methods, Large Language Models (LLMs), and AI-based controllers does not indicate a weakness in well-established control theory. Instead, it aims to reduce dependence on models and uncertainties, address increasingly complex systems, and potentially achieve decision-making capabilities comparable to human-level performance. This revolution integrates knowledge from computer science, machine learning, biology, and classical control, producing promising algorithms that are yet to demonstrate widespread real-world applicability. Despite the maturity of control theory and the presence of various performance criteria, there is still a lack of standardised metrics for testing, evaluation, Verification and Validation ($V\&V$) of algorithms. This gap can lead to algorithms that, while optimal in certain aspects, may fall short of practical implementation, sparking debates within the literature. For a controller to succeed in real-world applications, it must satisfy three key categories of performance metrics: tracking quality, control effort (energy consumption), and robustness. This paper rather takes an applied perspective, proposing and consolidating standard performance criteria for testing and analysing control systems, intended for researchers and students. The proposed framework ensures the post-design applicability of a black-box algorithm, aligning with modern data analysis and $V\&V$ perspectives to prevent resource allocation to systems with limited impact or imprecise claims.
Online ResNet-Based Adaptive Control for Nonlinear Target Tracking
Authors: Cristian F. Nino, Omkar Sudhir Patil, Jordan C. Insinger, Marla R. Eisman, Warren E. Dixon
Date: 2025-03-18 00:00:00

A generalized ResNet architecture for adaptive control of nonlinear systems with black box uncertainties is developed. The approach overcomes limitations in existing methods by incorporating pre-activation shortcut connections and a zeroth layer block that accommodates different input-output dimensions. The developed Lyapunov-based adaptation law establishes exponential convergence to a neighborhood of the target state despite unknown dynamics and disturbances. Furthermore, the theoretical results are validated through a comparative experiment.
An Assessment of the UK Government Clean Energy Strategy for the Year 2030
Authors: Anthony D. Stephens, David R. Walwyn
Date: 2025-03-18 00:00:00

In 2024, the UK Government made two striking announcements on its plans to decarbonise the energy system; it pledged GBP22 billion to establish carbon capture and storage hubs on Teesside and Merseyside and released the Clean Power 2030 Action Plan. This paper questions the validity of both plans, arguing that they do not take adequate account of the consequences of the highly variable nature of wind and solar generations. Using dynamic models of future UK electricity systems which are designed to take account of these variabilities, it is shown that the Clean Power 2030 Action Plan overestimates the ability of wind and solar generations to decarbonise the electricity system as they increase in size relative to the demand of the electricity system. More importantly, the dynamic models show that most of the achievable decarbonization is the result of increasing wind generation from the current level of around 10 GW to around 20 GW. Increasing wind generation to only 20 GW, rather than to 30 GW as proposed in the Action Plan, should halve the proposed cost, a saving of perhaps GBP 120 billion, with little disbenefit in terms of reduced decarbonization. Furthermore, the dynamic modelling shows that UK gas storage capacity of 7.5 winter days looks hopeless inadequate in comparison with the storage capacities deemed necessary by its continental neighbors. Concern is expressed that a consequence of the Climate Change Act of 2008 requiring the UK to meet arbitrary decarbonization targets is leading government advisors to propose several unproven and therefore highly risky technological solutions.
Improved Scalable Lipschitz Bounds for Deep Neural Networks
Authors: Usman Syed, Bin Hu
Date: 2025-03-18 00:00:00

Computing tight Lipschitz bounds for deep neural networks is crucial for analyzing their robustness and stability, but existing approaches either produce relatively conservative estimates or rely on semidefinite programming (SDP) formulations (namely the LipSDP condition) that face scalability issues. Building upon ECLipsE-Fast, the state-of-the-art Lipschitz bound method that avoids SDP formulations, we derive a new family of improved scalable Lipschitz bounds that can be combined to outperform ECLipsE-Fast. Specifically, we leverage more general parameterizations of feasible points of LipSDP to derive various closed-form Lipschitz bounds, avoiding the use of SDP solvers. In addition, we show that our technique encompasses ECLipsE-Fast as a special case and leads to a much larger class of scalable Lipschitz bounds for deep neural networks. Our empirical study shows that our bounds improve ECLipsE-Fast, further advancing the scalability and precision of Lipschitz estimation for large neural networks.
Cross-Environment Transfer Learning for Location-Aided Beam Prediction in 5G and Beyond Millimeter-Wave Networks
Authors: Enrico Tosi, Panwei Hu, Aleksandar Ichkov, Marina Petrova, Ljiljana Simić
Date: 2025-03-18 00:00:00
Tasks: Beam Prediction, Transfer Learning

Millimeter-wave (mm-wave) communications requirebeamforming and consequent precise beam alignmentbetween the gNodeB (gNB) and the user equipment (UE) toovercome high propagation losses. This beam alignment needs tobe constantly updated for different UE locations based on beamsweepingradio frequency measurements, leading to significantbeam management overhead. One potential solution involvesusing machine learning (ML) beam prediction algorithms thatleverage UE position information to select the serving beamwithout the overhead of beam sweeping. However, the highlysite-specific nature of mm-wave propagation means that MLmodels require training from scratch for each scenario, whichis inefficient in practice. In this paper, we propose a robustcross-environment transfer learning solution for location-aidedbeam prediction, whereby the ML model trained on a referencegNB is transferred to a target gNB by fine-tuning with a limiteddataset. Extensive simulation results based on ray-tracing in twourban environments show the effectiveness of our solution forboth inter- and intra-city model transfer. Our results show thatby training the model on a reference gNB and transferring themodel by fine-tuning with only 5% of the target gNB dataset,we can achieve 80% accuracy in predicting the best beamfor the target gNB. Importantly, our approach improves thepoor generalization accuracy of transferring the model to newenvironments without fine-tuning by around 75 percentage points.This demonstrates that transfer learning enables high predictionaccuracy while reducing the computational and training datasetcollection burden of ML-based beam prediction, making itpractical for 5G-and-beyond deployments.
Vanishing Stacked-Residual PINN for State Reconstruction of Hyperbolic Systems
Authors: Katayoun Eshkofti, Matthieu Barreau
Date: 2025-03-18 00:00:00

In a more connected world, modeling multi-agent systems with hyperbolic partial differential equations (PDEs) offers a compact, physics-consistent description of collective dynamics. However, classical control tools need adaptation for these complex systems. Physics-informed neural networks (PINNs) provide a powerful framework to fix this issue by inferring solutions to PDEs by embedding governing equations into the neural network. A major limitation of original PINNs is their inability to capture steep gradients and discontinuities in hyperbolic PDEs. To tackle this problem, we propose a stacked residual PINN method enhanced with a vanishing viscosity mechanism. Initially, a basic PINN with a small viscosity coefficient provides a stable, low-fidelity solution. Residual correction blocks with learnable scaling parameters then iteratively refine this solution, progressively decreasing the viscosity coefficient to transition from parabolic to hyperbolic PDEs. Applying this method to traffic state reconstruction improved results by an order of magnitude in relative $\mathcal{L}^2$ error, demonstrating its potential to accurately estimate solutions where original PINNs struggle with instability and low fidelity.
Decentralized Continuification Control of Multi-Agent Systems via Distributed Density Estimation
Authors: Beniamino Di Lorenzo, Gian Carlo Maffettone, Mario di Bernardo
Date: 2025-03-18 00:00:00
Tasks: Density Estimation

This paper introduces a novel decentralized implementation of a continuification-based strategy to control the density of large-scale multi-agent systems on the unit circle. While continuification methods effectively address micro-to-macro control problems by reformulating ordinary/stochastic differential equations (ODEs/SDEs) agent-based models into more tractable partial differential equations (PDEs), they traditionally require centralized knowledge of macroscopic state observables. We overcome this limitation by developing a distributed density estimation framework that combines kernel density estimation with PI consensus dynamics. Our approach enables agents to compute local density estimates and derive local control actions using only information from neighboring agents in a communication network. Numerical validations across multiple scenarios - including regulation, tracking, and time-varying communication topologies - confirm the effectiveness of the proposed approach. They also convincingly demonstrate that our decentralized implementation achieves performance comparable to centralized approaches while enhancing reliability and practical applicability.
Modeling, Analysis, and Optimization of Cascaded Power Amplifiers
Authors: Oksana Moryakova, Thomas Eriksson, Håkan Johansson
Date: 2025-03-18 00:00:00

This paper deals with modeling, analysis, and optimization of power amplifiers (PAs) placed in a cascaded structure, particularly the effect of cascaded nonlinearities is studied by showing potential ways to minimize the total nonlinearities. The nonlinear least-squares algorithm is proposed to optimize the PA parameters along with the input power level, and thereby minimize the total nonlinearities in the cascaded structure. The simulation results demonstrate that the performance of the optimized configurations for up to five PAs using the proposed framework can improve the linearity properties of the overall cascade.
Modular Distributed Nonconvex Learning with Error Feedback
Authors: Guido Carnevale, Nicola Bastianello
Date: 2025-03-18 00:00:00
Tasks: Computational Efficiency

In this paper, we design a novel distributed learning algorithm using stochastic compressed communications. In detail, we pursue a modular approach, merging ADMM and a gradient-based approach, benefiting from the robustness of the former and the computational efficiency of the latter. Additionally, we integrate a stochastic integral action (error feedback) enabling almost sure rejection of the compression error. We analyze the resulting method in nonconvex scenarios and guarantee almost sure asymptotic convergence to the set of stationary points of the problem. This result is obtained using system-theoretic tools based on stochastic timescale separation. We corroborate our findings with numerical simulations in nonconvex classification.
ON-Traffic: An Operator Learning Framework for Online Traffic Flow Estimation and Uncertainty Quantification from Lagrangian Sensors
Authors: Jake Rap, Amritam Das
Date: 2025-03-18 00:00:00
Tasks: Management, Operator learning, Uncertainty Quantification

Accurate traffic flow estimation and prediction are critical for the efficient management of transportation systems, particularly under increasing urbanization. Traditional methods relying on static sensors often suffer from limited spatial coverage, while probe vehicles provide richer, albeit sparse and irregular data. This work introduces ON-Traffic, a novel deep operator Network and a receding horizon learning-based framework tailored for online estimation of spatio-temporal traffic state along with quantified uncertainty by using measurements from moving probe vehicles and downstream boundary inputs. Our framework is evaluated in both numerical and simulation datasets, showcasing its ability to handle irregular, sparse input data, adapt to time-shifted scenarios, and provide well-calibrated uncertainty estimates. The results demonstrate that the model captures complex traffic phenomena, including shockwaves and congestion propagation, while maintaining robustness to noise and sensor dropout. These advancements present a significant step toward online, adaptive traffic management systems.
Automotive Battery Pack Standards and Design Characteristics: A Review
Authors: Saeid Haghbin, Morteza Rezaei Larijani, MohammadReza Zolghadri, Shahin Hedayati Kia
Date: 2025-03-16 00:00:00

The latest advancements and near-future trends in automotive battery packs, underlying regulatory compliance, and performance requirements are presented in this paper. In response to these specifications, high-level solutions that converge towards a standard architecture for passenger cars are provided. Transition to high-voltage enables ultra-fast charging above 350 kW, which reduces the charging times to less than 20 minutes. Also, advances in energy density and battery capacities make advancements in enhancing the electric vehicle's range beyond 1000 km per charge. Key factors such as electrical performance, safety, mechanical integrity, reliability, endurance, environmental conditions, and diagnostics are examined. This study explores the next generation of cost-effective and high-performance battery systems and discovers near-future battery technologies, including sodium-ion chemistry and rare-earth-free alternatives, as well as battery applications in aviation.
Identification of non-causal systems with random switching modes (Extended Version)
Authors: Yanxin Zhang, Chengpu Yu, Filippo Fabiani
Date: 2025-03-18 00:00:00
Tasks: Management, parameter estimation

We consider the identification of non-causal systems with random switching modes (NCSRSM), a class of models essential for describing typical power load management and department store inventory dynamics. The simultaneous identification of causal-andanticausal subsystems, along with the presence of random switching sequences, however, make the overall identification problem particularly challenging. To this end, we develop an expectation-maximization (EM) based system identification technique, where the E-step proposes a modified Kalman filter (KF) to estimate the states and switching sequences of causal-and-anticausal subsystems, while the M-step consists in a switching least-squares algorithm to estimate the parameters of individual subsystems. We establish the main convergence features of the proposed identification procedure, also providing bounds on the parameter estimation errors under mild conditions. Finally, the effectiveness of our identification method is validated through two numerical simulations.
What was Said, What was not Said
Authors: Hamid Jahanian
Date: 2025-03-18 00:00:00

In the process industry, the configuration of Safety Instrumented Systems (SIS) must comply with a defined set of safety requirements, typically documented in the Safety Requirements Specification (SRS). The functional safety standard IEC 61511 outlines the necessary content and quality criteria for the SRS. However, developing an effective SRS can be challenging. This article examines some of these challenges and proposes good practices to address them. It discusses SRS ownership, "staged" development of SRS, and the classification and traceability of requirements. Additionally, it explores the issue of untold "negative" requirements and suggests exploratory "inspection" of SIS Application Programs (APs) as a potential remedy.
Geometry of the Feasible Output Regions of Grid-Interfacing Inverters with Current Limits
Authors: Lauren Streitmatter, Trager Joswig-Jones, Baosen Zhang
Date: 2025-03-18 00:00:00

Many resources in the grid connect to power grids via programmable grid-interfacing inverters that can provide grid services and offer greater control flexibility and faster response times compared to synchronous generators. However, the current through the inverter needs to be limited to protect the semiconductor components. Existing controllers are designed using somewhat ad hoc methods, for example, by adding current limiters to preexisting control loops, which can lead to stability issues or overly conservative operations. In this paper, we study the geometry of the feasible output region of a current-limited inverter. We show that under a commonly used model, the feasible region is convex. We provide an explicit characterization of this region, which allows us to efficiently find the optimal operating points of the inverter. We demonstrate how knowing the feasible set and its convexity allows us to improve upon existing grid-forming inverters such that their steady-state currents always remain within the current magnitude limit, whereas standard grid-forming controllers can lead to instabilities and violations.
A finite-sample bound for identifying partially observed linear switched systems from a single trajectory
Authors: Daniel Racz, Mihaly Petreczky, Balint Daroczy
Date: 2025-03-17 00:00:00
Tasks: parameter estimation

We derive a finite-sample probabilistic bound on the parameter estimation error of a system identification algorithm for Linear Switched Systems. The algorithm estimates Markov parameters from a single trajectory and applies a variant of the Ho-Kalman algorithm to recover the system matrices. Our bound guarantees statistical consistency under the assumption that the true system exhibits quadratic stability. The proof leverages the theory of weakly dependent processes. To the best of our knowledge, this is the first finite-sample bound for this algorithm in the single-trajectory setting.
From Autonomous Agents to Integrated Systems, A New Paradigm: Orchestrated Distributed Intelligence
Authors: Krti Tallam
Date: 2025-03-17 00:00:00
Tasks: Decision Making

The rapid evolution of artificial intelligence (AI) has ushered in a new era of integrated systems that merge computational prowess with human decision-making. In this paper, we introduce the concept of Orchestrated Distributed Intelligence (ODI), a novel paradigm that reconceptualizes AI not as isolated autonomous agents, but as cohesive, orchestrated networks that work in tandem with human expertise. ODI leverages advanced orchestration layers, multi-loop feedback mechanisms, and a high cognitive density framework to transform static, record-keeping systems into dynamic, action-oriented environments. Through a comprehensive review of multi-agent system literature, recent technological advances, and practical insights from industry forums, we argue that the future of AI lies in integrating distributed intelligence within human-centric workflows. This approach not only enhances operational efficiency and strategic agility but also addresses challenges related to scalability, transparency, and ethical decision-making. Our work outlines key theoretical implications and presents a practical roadmap for future research and enterprise innovation, aiming to pave the way for responsible and adaptive AI systems that drive sustainable innovation in human organizations.
Data-Driven Estimation of Structured Singular Values
Authors: Margarita A. Guerrero, Braghadeesh Lakshminarayanan, Cristian R. Rojas
Date: 2025-03-17 00:00:00

Estimating the size of the modeling error is crucial for robust control. Over the years, numerous metrics have been developed to quantify the model error in a control relevant manner. One of the most important such metrics is the structured singular value, as it leads to necessary and sufficient conditions for ensuring stability and robustness in feedback control under structured model uncertainty. Although the computation of the structured singular value is often intractable, lower and upper bounds for it can often be obtained if a model of the system is known. In this paper, we introduce a fully data-driven method to estimate a lower bound for the structured singular value, by conducting experiments on the system and applying power iterations to the collected data. Our numerical simulations demonstrate that this method effectively lower bounds the structured singular value, yielding results comparable to those obtained using the Robust Control toolbox of MATLAB.