pwc-archive/papers-with-abstracts huggingface dataset:
Average Consensus over Directed Networks in Open Multi-Agent Systems with Acknowledgement Feedback
Authors: Evagoras Makridis, Andreas Grammenos, Gabriele Oliva, Evangelia Kalyvianaki, Christoforos N. Hadjicostis, Themistoklis Charalambous
Date: 2024-09-13 00:00:00

In this paper, we address the distributed average consensus problem over directed networks in open multi-agent systems (OMAS), where the stability of the network is disrupted by frequent agent arrivals and departures, leading to a time-varying average consensus target. To tackle this challenge, we introduce a novel ratio consensus algorithm (OPENRC) based on acknowledgement feedback, designed to be robust to agent arrivals and departures, as well as to unbalanced directed network topologies. We demonstrate that when all active agents execute the OPENRC algorithm, the sum of their state variables remains constant during quiescent epochs when the network remains unchanged. By assuming eventual convergence during such quiescent periods following persistent variations in system composition and size, we prove the convergence of the OPENRC algorithm using column-stochasticity and mass-preservation properties. Finally, we apply and evaluate our proposed algorithm in a simulated environment, where agents are departing from and arriving in the network to highlight its resilience against changes in the network size and topology.
Convex Reformulation of Information Constrained Linear State Estimation with Mixed-Binary Variables for Outlier Accommodation
Authors: Wang Hu, Zeyi Jiang, Hamed Mohsenian-Rad, Jay A. Farrell
Date: 2024-09-13 00:00:00
Tasks: Computational Efficiency, State Estimation

This article considers the challenge of accommodating outlier measurements in state estimation. The Risk-Averse Performance-Specified (RAPS) state estimation approach addresses outliers as a measurement selection Bayesian risk minimization problem subject to an information accuracy constraint, which is a non-convex optimization problem. Prior explorations into RAPS rely on exhaustive search, which becomes computationally infeasible as the number of measurements increases. This paper derives a convex formulation for the RAPS optimization problems via transforming the mixed-binary variables into linear constraints. The convex reformulation herein can be solved by convex programming toolboxes, significantly enhancing computational efficiency. We explore two specifications: Full-RAPS, utilizing the full information matrix, and Diag-RAPS, focusing on diagonal elements only. The simulation comparison demonstrates that Diag-RAPS is faster and more efficient than Full-RAPS. In comparison with Kalman Filter (KF) and Threshold Decisions (TD), Diag-RAPS consistently achieves the lowest risk, while achieving the performance specification when it is feasible.
Global Minimum Energy State Estimation for Embedded Nonlinear Systems with Symmetry
Authors: Pieter van Goor, Robert Mahony
Date: 2024-09-13 00:00:00
Tasks: State Estimation

Choosing a nonlinear state estimator for an application often involves a trade-off between local optimality (such as provided by an extended Kalman filter) and (almost-/semi-) global asymptotic stability (such as provided by a constructive observer design based on Lyapunov principles). This paper proposes a filter design methodology that is both global and optimal for a class of nonlinear systems. In particular, systems for which there is an embedding of the state-manifold into Euclidean space for which the measurement function is linear in the embedding space and for which there is a synchronous error construction. A novel observer is derived using the minimum energy filter design paradigm and exploiting the embedding coordinates to solve for the globally optimal solution exactly. The observer is demonstrated through an application to the problem of unit quaternion attitude estimation, by embedding the 3-dimensional nonlinear system into a 4-dimensional Euclidean space. Simulation results demonstrate that the state estimate remains optimal for all time and converges even with a very large initial error.
Generalization of Gershgorin's theorem. Analysis and design of control laws
Authors: Igor Furtat
Date: 2024-09-13 00:00:00

The application of the Gershgorin circle theorem and some of its derivatives to estimate the eigenvalues of a matrix is considered. The obtained results are developed to obtain the localization region of the eigenvalues of a matrix with interval-indefinite constant or non-stationary elements. The concept of e-circles is introduced to obtain more accurate estimates of these regions than when using Gershgorin circles. The obtained results are applied to the stability analysis of network systems, where it is shown that the proposed methods allow one to analyze a network with a much larger number of agents than when using the CVX, Yalmip, eig and lyap methods (functions in MatLab). It is further shown that if the obtained results are applied not to the system itself, but to the result obtained using the Lyapunov function method, then one can study systems with matrices without diagonal dominance. This made it possible to consider a modification of the Demidovich condition for systems with non-stationary parameters and design of a control law for non-stationary systems with matrices without diagonal dominance. All obtained results are illustrated by numerical modeling.
OIDM: An Observability-based Intelligent Distributed Edge Sensing Method for Industrial Cyber-Physical Systems
Authors: Shigeng Wang, Tiankai Jin, Yehan Ma, Cailian Chen
Date: 2024-09-13 00:00:00
Tasks: Deep Reinforcement Learning, Edge-computing, Scheduling

Industrial cyber-physical systems (ICPS) integrate physical processes with computational and communication technologies in industrial settings. With the support of edge computing technology, it is feasible to schedule large-scale sensors for efficient distributed sensing. In the sensing process, observability is the key to obtaining complete system states, and stochastic scheduling is more suitable considering uncertain factors in wireless communication. However, existing works have limited research on observability in stochastic scheduling. Targeting this issue, we propose an observability-based intelligent distributed edge sensing method (OIDM). Deep reinforcement learning (DRL) methods are adopted to optimize sensing accuracy and power efficiency. Based on the system's ability to achieve observability, we establish a bridge between observability and the number of successful sensor transmissions. Novel linear approximations of observability criteria are provided, and probabilistic bounds on observability are derived. Furthermore, these bounds guide the design of action space to achieve a probabilistic observability guarantee in stochastic scheduling. Finally, our proposed method is applied to the estimation of slab temperature in industrial hot rolling process, and simulation results validate its effectiveness.
Safety of Linear Systems under Severe Sensor Attacks
Authors: Xiao Tan, Pio Ong, Paulo Tabuada, Aaron D. Ames
Date: 2024-09-12 00:00:00
Tasks: State Estimation

Cyber-physical systems can be subject to sensor attacks, e.g., sensor spoofing, leading to unsafe behaviors. This paper addresses this problem in the context of linear systems when an omniscient attacker can spoof several system sensors at will. In this adversarial environment, existing results have derived necessary and sufficient conditions under which the state estimation problem has a unique solution. In this work, we consider a severe attacking scenario when such conditions do not hold. To deal with potential state estimation uncertainty, we derive an exact characterization of the set of all possible state estimates. Using the framework of control barrier functions, we propose design principles for system safety in offline and online phases. For the offline phase, we derive conditions on safe sets for all possible sensor attacks that may be encountered during system deployment. For the online phase, with past system measurements collected, a quadratic program-based safety filter is proposed to enforce system safety. A 2D-vehicle example is used to illustrate the theoretical results.
Social Equity Based Optimal Power Flow Framework to Hedge Against Price Events
Authors: Sachinth Viththarachchige, Demy Alexander, Sarangan Rajendran, Visvakumar Aravinthan
Date: 2024-09-12 00:00:00

With the increasing frequency of high impact low probability events, electricity markets are experiencing significant price spikes more often. This paper proposes a novel social equity driven optimal power flow framework to mitigate the adverse effects of price events that lead to such price spikes. The framework integrates social welfare optimization with socioeconomic considerations by including a socioeconomic score that quantifies the energy burden and socioeconomic status of consumers. By incorporating both supply cost and consumer satisfaction, the model aims to achieve a balanced and fair distribution of resources during price events, while considering resource scarcity and possible load curtailment. The proposed framework is tested for convergence on modified versions of the PJM 5-bus system and IEEE 24-bus reliability test system, discussing its potential effectiveness in enhancing social equity and optimizing power flow under system security constraints. Sensitivity analysis further highlights the impact of socioeconomic score on social welfare, providing insights for future improvements.
Harnessing On-Machine Metrology Data for Prints with a Surrogate Model for Laser Powder Directed Energy Deposition
Authors: Michael Juhasz, Eric Chin, Youngsoo Choi, Joseph T. McKeown, Saad Khairallah
Date: 2024-09-12 00:00:00
Tasks: Uncertainty Quantification

In this study, we leverage the massive amount of multi-modal on-machine metrology data generated from Laser Powder Directed Energy Deposition (LP-DED) to construct a comprehensive surrogate model of the 3D printing process. By employing Dynamic Mode Decomposition with Control (DMDc), a data-driven technique, we capture the complex physics inherent in this extensive dataset. This physics-based surrogate model emphasizes thermodynamically significant quantities, enabling us to accurately predict key process outcomes. The model ingests 21 process parameters, including laser power, scan rate, and position, while providing outputs such as melt pool temperature, melt pool size, and other essential observables. Furthermore, it incorporates uncertainty quantification to provide bounds on these predictions, enhancing reliability and confidence in the results. We then deploy the surrogate model on a new, unseen part and monitor the printing process as validation of the method. Our experimental results demonstrate that the predictions align with actual measurements with high accuracy, confirming the effectiveness of our approach. This methodology not only facilitates real-time predictions but also operates at process-relevant speeds, establishing a basis for implementing feedback control in LP-DED.
Optimal Design of Vehicle Dynamics Using Gradient-Based, Mixed-Fidelity Multidisciplinary Optimization
Authors: Hyunmin Cheong, Mehran Ebrahimi, Hesam Salehipour, Adrian Butscher, Alex Tessier
Date: 2024-09-11 00:00:00

In automotive engineering, designing for optimal vehicle dynamics is challenging due to the complexities involved in analysing the behaviour of a multibody system. Typically, a simplified set of dynamics equations for only the key bodies of the vehicle such as the chassis and wheels are formulated while reducing their degrees of freedom. In contrast, one could employ high-fidelity multibody dynamics simulation and include more intricate details such as the individual suspension components while considering full degrees of freedom for all bodies; however, this is more computationally demanding. Also, for gradient-based design optimization, computing adjoints for different objective functions can be more challenging for the latter approach, and often not feasible if an existing multibody dynamics solver is used. We propose a mixed-fidelity multidisciplinary approach, in which a simplified set of dynamics equations are used to model the whole vehicle while incorporating a high-fidelity multibody suspension module as an additional coupled discipline. We then employ MAUD (modular analysis and unified derivatives) to combine analytical derivatives based on the dynamics equations and finite differences obtained using an existing multibody solver. Also, we use a collocation method for time integration, which solves for both the system trajectory and optimal design variables simultaneously. The benefits of our approach are shown in an experiment conducted to find optimal vehicle parameters that optimize ride comfort and driving performance considering vertical vehicle dynamics.
A Dynamic Cooler Model for Cement Clinker Production
Authors: Jan Lorenz Svensen, Wilson Ricardo Leal da Silva, Javier Pigazo Merino, Dinesh Sampath, John Bagterp Jørgensen
Date: 2024-09-09 00:00:00
Tasks: model

We present a 2D model for a grate belt cooler in the pyro-section of a cement plant. The model is formulated as an index-1 differential-algebraic equation (DAE) model based on first engineering principles. The model systematically integrates thermo-physical aspects, transport phenomena, reaction kinetics, mass and energy balances, and algebraic volume and energy relations. The model is used for dynamic simulation of the cooler and the paper provides dynamic and steady-state simulation results matching the expected behavior. The cooler model is one part of a full pyro-section model for dynamical simulations. The model can serve as a basis for the design of optimization and control systems towards improving energy efficiency and CO2 emission.
A Systematic Procedure for Topological Path Identification with Raw Data Transformation in Electrical Distribution Networks
Authors: Maurizio Vassallo, Alireza Bahmanyar, Laurine Duchesne, Adrien Leerschool, Simon Gerard, Thomas Wehenkel, Damien Ernst
Date: 2024-09-09 00:00:00

This paper introduces a systematic approach to address the topological path identification (TPI) problem in power distribution networks. Our approach starts by listing the DSO's raw information coming from several sources. The raw information undergoes a transformation process using a set of transformation functions. This process converts the raw information into well-defined information exploitable by an algorithm. Then a set of hypothetical paths is generated, considering any potential connections between the elements of the power distribution system. This set of hypothetical paths is processed by the algorithm that identifies the hypothetical paths that are compatible with the well-defined information. This procedure operates iteratively, adapting the set of transformation functions based on the result obtained: if the identified paths fail to meet the DSO's expectations, new data is collected, and/or the transformation functions found to be responsible for the discrepancies are modified. The systematic procedure offers practical advantages for DSOs, including improved accuracy in path identification and high adaptability to diverse network configurations, even with incomplete or inaccurate data. Consequently, it emerges as a useful tool for the construction of digital twins of power distribution networks that aligns with DSO expectations.
Fair Reinforcement Learning Algorithm for PV Active Control in LV Distribution Networks
Authors: Maurizio Vassallo, Amina Benzerga, Alireza Bahmanyar, Damien Ernst
Date: 2024-09-09 00:00:00
Tasks: Fairness

The increasing adoption of distributed energy resources, particularly photovoltaic (PV) panels, has presented new and complex challenges for power network control. With the significant energy production from PV panels, voltage issues in the network have become a problem. Currently, PV smart inverters (SIs) are used to mitigate the voltage problems by controlling their active power generation and reactive power injection or absorption. However, reducing the active power output of PV panels can be perceived as unfair to some customers, discouraging future installations. To solve this issue, in this paper, a reinforcement learning technique is proposed to address voltage issues in a distribution network, while considering fairness in active power curtailment among customers. The feasibility of the proposed approach is explored through experiments, demonstrating its ability to effectively control voltage in a fair and efficient manner.
An Optimization Algorithm for Customer Topological Paths Identification in Electrical Distribution Networks
Authors: Maurizio Vassallo, Adrien Leerschool, Alireza Bahmanyar, Laurine Duchesne, Simon Gerard, Thomas Wehenkel, Damien Ernst
Date: 2024-09-09 00:00:00

A customer topological path represents the sequence of network elements connecting an MV/LV transformer to a customer. Accurate knowledge of these paths is crucial for distribution system operators (DSOs) in digitalization, analysis, and network planning. This paper introduces an innovative approach to address the challenge of customer topological path identification (TPI) using only the limited and often inaccurate data available to DSOs. Specifically, our method relies only on geographic information system (GIS) data of network elements and the customer to MV/LV transformers connection information. We introduce an integer linear programming (ILP) optimization algorithm designed to identify customer topological paths that closely approximate the real electricity paths. The effectiveness of the proposed approach is demonstrated through its application to both an academic and a real-world electrical distribution network. Results show that the method effectively addresses data inaccuracies and successfully identifies customer topological paths, providing a valuable tool for DSOs in developing accurate digital twins of their distribution networks.
Learning-Enabled Iterative Convex Optimization for Safety-Critical Model Predictive Control
Authors: Shuo Liu, Zhe Huang, Jun Zeng, Koushil Sreenath, Calin A. Belta
Date: 2024-09-08 00:00:00
Tasks: Model Predictive Control

Safety remains a central challenge in control of dynamical systems, particularly when the boundaries of unsafe sets are complex or unknown. This paper proposes a learning-enabled framework for safety-critical Model Predictive Control (MPC) that integrates Discrete-Time High-Order Control Barrier Functions (DHOCBFs) with iterative convex optimization. Unlike existing methods that primarily address CBFs of relative degree one with fully known unsafe set boundaries, our approach generalizes to arbitrary relative degrees and addresses scenarios where the unsafe set boundaries must be inferred. We extract pixel-based data specifically from unsafe set boundaries and train a neural network to approximate local linearizations of these boundaries. The learned models are incorporated into the linearized DHOCBF constraints at each time step, enabling real-time constraint satisfaction within the MPC framework. An iterative convex optimization procedure is developed to accelerate computation while maintaining formal safety guarantees. The benefits of computational performance and safe avoidance of obstacles with diverse shapes are examined and confirmed through numerical results. By bridging model-based control with learning-based environment modeling, this framework advances safe autonomy for discrete-time systems operating in complex and partially known settings.
Effects of synapse location, delay and background stochastic activity on synchronising hippocampal CA1 neurons
Authors: Alessandro Fiasconaro, Michele Migliore
Date: 2024-09-16 00:00:00

We study the synchronisation of neurons in a realistic model under the Hodgkin-Huxley dynamics. To focus on the role of the different locations of the excitatory synapses, we use two identical neurons where the set of input signals is grouped at two different distances from the soma. The system is intended to represent a CA1 hippocampal neuron in which the synapses arriving from the CA3 neurons of the trisynaptic pathway appear to be localised in the apical dendritic region and are, in principle, either proximal or distal to the soma. Synchronisation is studied using a specifically defined spiking correlation function as a function of various parameters such as the distance from the soma of one of the synaptic groups, the inhibition weight and the associated activation delay. We found that the neurons' spiking activity depends nonmonotonically on the relative dendritic location of the synapses and their inhibitory weight, whereas the synchronisation measure always decreases with inhibition, and strongly depends on its activation time delay. The background activity on the somas results essentially independent on the fluctuation intensity and strongly support the importance of the balance between inhibition and excitation for neuronal synchronization.
Hippocampal synchronization in a realistic CA1 neuron model
Authors: Alessandro Fiasconaro, Michele Migliore
Date: 2024-09-16 00:00:00
Tasks: model

This work delves into studying the synchronization in two realistic neuron models using Hodgkin-Huxley dynamics. Unlike simplistic point-like models, excitatory synapses are here randomly distributed along the dendrites, introducing strong stochastic contributions into their signal propagation. To focus on the role of different excitatory positions, we use two copies of the same neuron whose synapses are located at different distances from the soma and are exposed to identical Poissonian distributed current pulses. The synchronization is investigated through a specifically defined spiking correlation function, and its behavior is analyzed as a function of several parameters: inhibition weight, distance from the soma of one synaptic group, excitatory inactivation delay, and weight of the excitatory synapses.
How Combined Pairwise and Higher-Order Interactions Shape Transient Dynamics
Authors: Sourin Chatterjee, Sayantan Nag Chowdhury
Date: 2024-09-14 00:00:00

Understanding how species interactions shape biodiversity is a core challenge in ecology. While much focus has been on long-term stability, there is rising interest in transient dynamics-the short-lived periods when ecosystems respond to disturbances and adjust toward stability. These transitions are crucial for predicting ecosystem reactions and guiding effective conservation. Our study introduces a model that uses convex combinations to blend pairwise and higher-order interactions, offering a more realistic view of natural ecosystems. We find pairwise interactions slow the journey to stability, while higher-order interactions speed it up. Employing global stability analysis and numerical simulations, we establish that as the proportion of higher-order interactions (HOIs) increases, mean transient times exhibit a significant reduction, thereby underscoring the essential role of HOIs in enhancing biodiversity stabilization. Our results reveal a robust correlation between the most negative real part of the eigenvalues of the Jacobian matrix associated with the linearized system at the coexistence equilibrium and the mean transient times. This indicates that a more negative leading eigenvalue correlates with accelerated convergence to stable coexistence abundances. This insight is vital for comprehending ecosystem resilience and recovery, emphasizing the key role of HOIs in promoting stabilization. Amid growing interest in transient dynamics and its implications for biodiversity and ecological stability, our study enhances the understanding of how species interactions affect both transient and long-term ecosystem behavior. By addressing a critical gap in ecological theory and offering a practical framework for ecosystem management, our work advances knowledge of transient dynamics, ultimately informing effective conservation strategies.