pwc-archive/papers-with-abstracts huggingface dataset:
Online 4D Ultrasound-Guided Robotic Tracking Enables 3D Ultrasound Localisation Microscopy with Large Tissue Displacements
Authors: Jipeng Yan, Qingyuan Tan, Shusei Kawara, Jingwen Zhu, Bingxue Wang, Matthieu Toulemonde, Honghai Liu, Ying Tan, Meng-Xing Tang
Date: 2024-09-17 00:00:00
Tasks: Super-Resolution

Super-Resolution Ultrasound (SRUS) imaging through localising and tracking microbubbles, also known as Ultrasound Localisation Microscopy (ULM), has demonstrated significant potential for reconstructing microvasculature and flows with sub-diffraction resolution in clinical diagnostics. However, imaging organs with large tissue movements, such as those caused by respiration, presents substantial challenges. Existing methods often require breath holding to maintain accumulation accuracy, which limits data acquisition time and ULM image saturation. To improve image quality in the presence of large tissue movements, this study introduces an approach integrating high-frame-rate ultrasound with online precise robotic probe control. Tested on a microvasculature phantom with translation motions up to 20 mm, twice the aperture size of the matrix array used, our method achieved real-time tracking of the moving phantom and imaging volume rate at 85 Hz, keeping majority of the target volume in the imaging field of view. ULM images of the moving cross channels in the phantom were successfully reconstructed in post-processing, demonstrating the feasibility of super-resolution imaging under large tissue motions. This represents a significant step towards ULM imaging of organs with large motion.
A Scalable Game Theoretic Approach for Coordination of Multiple Dynamic Systems
Authors: Mostafa M. Shibl, Vijay Gupta
Date: 2024-09-17 00:00:00

Learning in games provides a powerful framework to design control policies for self-interested agents that may be coupled through their dynamics, costs, or constraints. We consider the case where the dynamics of the coupled system can be modeled as a Markov potential game. In this case, distributed learning by the agents ensures that their control policies converge to a Nash equilibrium of this game. However, typical learning algorithms such as natural policy gradient require knowledge of the entire global state and actions of all the other agents, and may not be scalable as the number of agents grows. We show that by limiting the information flow to a local neighborhood of agents in the natural policy gradient algorithm, we can converge to a neighborhood of optimal policies. If the game can be designed through decomposing a global cost function of interest to a designer into local costs for the agents such that their policies at equilibrium optimize the global cost, this approach can be of interest to team coordination problems as well. We illustrate our approach through a sensor coverage problem.
Constrained Learning for Decentralized Multi-Objective Coverage Control
Authors: Juan Cervino, Saurav Agarwal, Vijay Kumar, Alejandro Ribeiro
Date: 2024-09-17 00:00:00

The multi-objective coverage control problem requires a robot swarm to collaboratively provide sensor coverage to multiple heterogeneous importance density fields IDFs simultaneously. We pose this as an optimization problem with constraints and study two different formulations: (1) Fair coverage, where we minimize the maximum coverage cost for any field, promoting equitable resource distribution among all fields; and (2) Constrained coverage, where each field must be covered below a certain cost threshold, ensuring that critical areas receive adequate coverage according to predefined importance levels. We study the decentralized setting where robots have limited communication and local sensing capabilities, making the system more realistic, scalable, and robust. Given the complexity, we propose a novel decentralized constrained learning approach that combines primal-dual optimization with a Learnable Perception-Action-Communication (LPAC) neural network architecture. We show that the Lagrangian of the dual problem can be reformulated as a linear combination of the IDFs, enabling the LPAC policy to serve as a primal solver. We empirically demonstrate that the proposed method (i) significantly outperforms state-of-the-art decentralized controllers by 30% on average in terms of coverage cost, (ii) transfers well to larger environments with more robots, and (iii) scalable in the number of IDFs and robots in the swarm.
To What Extent do Open-loop and Feedback Nash Equilibria Diverge in General-Sum Linear Quadratic Dynamic Games?
Authors: Chih-Yuan Chiu, Jingqi Li, Maulik Bhatt, Negar Mehr
Date: 2024-09-17 00:00:00

Dynamic games offer a versatile framework for modeling the evolving interactions of strategic agents, whose steady-state behavior can be captured by the Nash equilibria of the games. Nash equilibria are often computed in feedback, with policies depending on the state at each time, or in open-loop, with policies depending only on the initial state. Empirically, open-loop Nash equilibria (OLNE) could be more efficient to compute, while feedback Nash equilibria (FBNE) often encode more complex interactions. However, it remains unclear exactly which dynamic games yield FBNE and OLNE that differ significantly and which do not. To address this problem, we present a principled comparison study of OLNE and FBNE in linear quadratic (LQ) dynamic games. Specifically, we prove that the OLNE strategies of an LQ dynamic game can be synthesized by solving the coupled Riccati equations of an auxiliary LQ game with perturbed costs. The construction of the auxiliary game allows us to establish conditions under which OLNE and FBNE coincide and derive an upper bound on the deviation between FBNE and OLNE of an LQ game.
Bearing-based Target Localisation in Search and Rescue Scenarios
Authors: Giulia Michieletto, Nicola Mimmo, Roberto Naldi, Angelo Cenedese
Date: 2024-09-17 00:00:00

This paper deals with the target localisation problem in search and rescue scenarios in which the technology is based on electromagnetic transceivers. The noise floor and the shape of the electromagnetic radiation pattern make this problem challenging. Indeed, on the one hand, the signal-to-noise ratio reduces with the inverse of the distance from the electromagnetic source thus impacting estimation-based techniques applicability. On the other hand, non-isotropic radiation patterns lessen the efficacy of gradient-based policies. In this work, we manage a fleet of autonomous agents, equipped with electromagnetic sensors, by combining gradient-based and estimation-based techniques to speed up the transmitter localisation. Simulations specialized in the ARTVA technology used in search and rescue in avalanche scenarios confirm that our scheme outperforms current solutions.
Preventing Unconstrained CBF Safety Filters Caused by Invalid Relative Degree Assumptions
Authors: Lukas Brunke, SiQi Zhou, Angela P. Schoellig
Date: 2024-09-17 00:00:00

Control barrier function (CBF)-based safety filters are used to certify and modify potentially unsafe control inputs to a system such as those provided by a reinforcement learning agent or a non-expert user. In this context, safety is defined as the satisfaction of state constraints. Originally designed for continuous-time systems, CBF safety filters typically assume that the system's relative degree is well-defined and is constant across the domain; however, this assumption is restrictive and rarely verified -- even linear system dynamics with a quadratic CBF candidate may not satisfy this assumption. In real-world applications, continuous-time CBF safety filters are implemented in discrete time, exacerbating issues related to violating the condition on the relative degree. These violations can lead to the safety filter being unconstrained (any control input may be certified) for a finite time interval and result in chattering issues and constraint violations. We propose an alternative formulation to address these challenges. Specifically, we present a theoretically sound method that employs multiple CBFs to generate bounded control inputs at each state within the safe set, thereby preventing incorrect certification of arbitrary control inputs. Using this approach, we derive conditions on the maximum sampling time to ensure safety in discrete-time implementations. We demonstrate the effectiveness of our proposed method through simulations and real-world quadrotor experiments, successfully preventing chattering and constraint violations. Finally, we discuss the implications of violating the relative degree condition on CBF synthesis and learning-based CBF methods.
Data-driven Dynamic Intervention Design in Network Games
Authors: Xiupeng Chen, Nima Monshizadeh
Date: 2024-09-17 00:00:00

Targeted interventions in games present a challenging problem due to the asymmetric information available to the regulator and the agents. This note addresses the problem of steering the actions of self-interested agents in quadratic network games towards a target action profile. A common starting point in the literature assumes prior knowledge of utility functions and/or network parameters. The goal of the results presented here is to remove this assumption and address scenarios where such a priori knowledge is unavailable. To this end, we design a data-driven dynamic intervention mechanism that relies solely on historical observations of agent actions and interventions. Additionally, we modify this mechanism to limit the amount of interventions, thereby considering budget constraints. Analytical convergence guarantees are provided for both mechanisms, and a numerical case study further demonstrates their effectiveness.
Application of Log-Linear Dynamic Inversion Control to a Multi-rotor
Authors: Li-Yu Lin, James Goppert, Inseok Hwang
Date: 2024-09-17 00:00:00

This paper presents an approach that employs log-linearization in Lie group theory and the Newton-Euler equations to derive exact linear error dynamics for a multi-rotor model, and applies this model with a novel log-linear dynamic inversion controller to simplify the nonlinear distortion and enhance the robustness of the log-linearized system. In addition, we utilize Linear Matrix Inequalities (LMIs) to bound the tracking error for the log-linearization in the presence of bounded disturbance input and use the exponential map to compute the invariant set of the nonlinear system in the Lie group. We demonstrate the effectiveness of our method via an illustrative example of a multi-rotor system with a reference trajectory, and the result validates the safety guarantees of the tracking error in the presence of bounded disturbance.
Distributed Optimization for Traffic Light Control and Connected Automated Vehicle Coordination in Mixed-Traffic Intersections
Authors: Viet-Anh Le, Andreas A. Malikopoulos
Date: 2024-09-17 00:00:00
Tasks: Distributed Optimization, Management

In this paper, we consider the problem of coordinating traffic light systems and connected automated vehicles (CAVs) in mixed-traffic intersections. We aim to develop an optimization-based control framework that leverages both the coordination capabilities of CAVs at higher penetration rates and intelligent traffic management using traffic lights at lower penetration rates. Since the resulting optimization problem is a multi-agent mixed-integer quadratic program, we propose a penalization-enhanced maximum block improvement algorithm to solve the problem in a distributed manner. The proposed algorithm, under certain mild conditions, yields a feasible person-by-person optimal solution of the centralized problem. The performance of the control framework and the distributed algorithm is validated through simulations across various penetration rates and traffic volumes.
Pose estimation of CubeSats via sensor fusion and Error-State Extended Kalman Filter
Authors: Deep Parikh, Manoranjan Majji
Date: 2024-09-17 00:00:00
Tasks: Pose Estimation, Sensor Fusion

A pose estimation technique based on error-state extended Kalman that fuses angular rates, accelerations, and relative range measurements is presented in this paper. An unconstrained dynamic model with kinematic coupling for a thrust-capable satellite is considered for the state propagation, and a pragmatic measurement model of the rate gyroscope, accelerometer, and an ultra-wideband radio are leveraged for the measurement update. The error-state extended Kalman filter framework is formulated for pose estimation, and its performance has been analyzed via several simulation scenarios. An application of the pose estimator for proximity operations and scaffolding formation of CubeSat deputies relative to their mother-ship is outlined. Finally, the performance of the error-state extended Kalman filter is demonstrated using experimental analysis consisting of a 3-DOF thrust cable satellite mock-up, rate gyroscope, accelerometer, and ultra-wideband radar modules.
Recurrent Graph Transformer Network for Multiple Fault Localization in Naval Shipboard Systems
Authors: Quang-Ha Ngo, Isabel Barnola, Tuyen Vu, Jianhua Zhang, Harsha Ravindra, Karl Schoder, Herbert Ginn
Date: 2024-09-16 00:00:00
Tasks: Diagnostic, energy management, Fault Detection, Fault Diagnosis, Fault localization, Graph Neural Network, Management

The integration of power electronics building blocks in modern MVDC 12kV Naval ship systems enhances energy management and functionality but also introduces complex fault detection and control challenges. These challenges strain traditional fault diagnostic methods, making it difficult to detect and manage faults across multiple locations while maintaining system stability and performance. This paper proposes a temporal recurrent graph transformer network for fault diagnosis in naval MVDC 12kV shipboard systems. The deep graph neural network uses gated recurrent units to capture temporal features and a multi-head attention mechanism to extract spatial features, enhancing diagnostic accuracy. The approach effectively identifies and evaluates successive multiple faults with high precision. The method is implemented and validated on the MVDC 12kV shipboard system designed by the ESDRC team, incorporating all key components. Results show significant improvements in fault localization accuracy, with a 1-4% increase in performance metrics compared to other machine learning methods.
Power Oscillation Damping Controllers for Grid-Forming Power Converters in Modern PowerSystems
Authors: Elia Mateu-Barriendos, Onur Alican, Javier Renedo, Carlos Collados-Rodriguez, Macarena Martin, Edgar Nuño, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt
Date: 2024-09-16 00:00:00

Inter-area oscillations have been extensively studied in conventional power systems dominated by synchronous machines, as well as methods to mitigate them. Several publications have addressed Power Oscillation Damping (POD) controllers in grid-following voltage source converters (GFOL). However, the performance of POD controllers for Grid-Forming voltage source converters (GFOR) in modern power systems with increased penetration of power electronics requires further investigation. This paper investigates the performance of GFORs and supplementary POD controllers in the damping of electromechanical oscillations in modern power systems. This paper proposes POD controllers in GFORs by supplementary modulation of active- and reactive-power injections of the converter and both simultaneously (POD- P, POD-Q and POD-PQ, respectively). The proposed POD controllers use the frequency imposed by the GFOR as the input signal, which has a simple implementation and it eliminates the need for additional measurements. Eigenvalue-sensitivity methods using a synthetic test system are applied to the design of POD controllers in GFORs, which is useful when limited information of the power system is available. This paper demonstrates the effectiveness of POD controllers in GFOR converters to damp electromechanical oscillations, by small-signal stability analysis and non-linear time-domain simulations in a small test system and in a large-scale power system.
A Model-Free Optimal Control Method With Fixed Terminal States and Delay
Authors: Mi Zhou, Erik Verriest, Chaouki Abdallah
Date: 2024-09-16 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Model-free algorithms are brought into the control system's research with the emergence of reinforcement learning algorithms. However, there are two practical challenges of reinforcement learning-based methods. First, learning by interacting with the environment is highly complex. Second, constraints on the states (boundary conditions) require additional care since the state trajectory is implicitly defined from the inputs and system dynamics. To address these problems, this paper proposes a new model-free algorithm based on basis functions, gradient estimation, and the Lagrange method. The favorable performance of the proposed algorithm is shown using several examples under state-dependent switches and time delays.
Direct Data-Driven Discounted Infinite Horizon Linear Quadratic Regulator with Robustness Guarantees
Authors: Ramin Esmzad, Hamidreza Modares
Date: 2024-09-16 00:00:00
Proceeding: Automatica 2025 2
Tasks: Data Driven Optimal Control, One-Shot Learning

This paper presents a one-shot learning approach with performance and robustness guarantees for the linear quadratic regulator (LQR) control of stochastic linear systems. Even though data-based LQR control has been widely considered, existing results suffer either from data hungriness due to the inherently iterative nature of the optimization formulation (e.g., value learning or policy gradient reinforcement learning algorithms) or from a lack of robustness guarantees in one-shot non-iterative algorithms. To avoid data hungriness while ensuing robustness guarantees, an adaptive dynamic programming formalization of the LQR is presented that relies on solving a Bellman inequality. The control gain and the value function are directly learned by using a control-oriented approach that characterizes the closed-loop system using data and a decision variable from which the control is obtained. This closed-loop characterization is noise-dependent. The effect of the closed-loop system noise on the Bellman inequality is considered to ensure both robust stability and suboptimal performance despite ignoring the measurement noise. To ensure robust stability, it is shown that this system characterization leads to a closed-loop system with multiplicative and additive noise, enabling the application of distributional robust control techniques. The analysis of the suboptimality gap reveals that robustness can be achieved without the need for regularization or parameter tuning. The simulation results on the active car suspension problem demonstrate the superiority of the proposed method in terms of robustness and performance gap compared to existing methods.
An Ultra-Low Power Wearable BMI System with Continual Learning Capabilities
Authors: Lan Mei, Thorir Mar Ingolfsson, Cristian Cioflan, Victor Kartsch, Andrea Cossettini, Xiaying Wang, Luca Benini
Date: 2024-09-16 00:00:00
Tasks: Continual Learning, EEG

Driven by the progress in efficient embedded processing, there is an accelerating trend toward running machine learning models directly on wearable Brain-Machine Interfaces (BMIs) to improve portability and privacy and maximize battery life. However, achieving low latency and high classification performance remains challenging due to the inherent variability of electroencephalographic (EEG) signals across sessions and the limited onboard resources. This work proposes a comprehensive BMI workflow based on a CNN-based Continual Learning (CL) framework, allowing the system to adapt to inter-session changes. The workflow is deployed on a wearable, parallel ultra-low power BMI platform (BioGAP). Our results based on two in-house datasets, Dataset A and Dataset B, show that the CL workflow improves average accuracy by up to 30.36% and 10.17%, respectively. Furthermore, when implementing the continual learning on a Parallel Ultra-Low Power (PULP) microcontroller (GAP9), it achieves an energy consumption as low as 0.45mJ per inference and an adaptation time of only 21.5ms, yielding around 25h of battery life with a small 100mAh, 3.7V battery on BioGAP. Our setup, coupled with the compact CNN model and on-device CL capabilities, meets users' needs for improved privacy, reduced latency, and enhanced inter-session performance, offering good promise for smart embedded real-world BMIs.
Higher-order interactions in random Lotka-Volterra communities
Authors: Laura Sidhom, Tobias Galla
Date: 2024-09-17 00:00:00
Tasks: Diversity

We use generating functionals to derive a dynamic mean-field description for generalised Lotka-Volterra systems with higher-order quenched random interactions. We use the resulting single effective species process to determine the stability diagram in the space of parameters specifying the statistics of interactions, and to calculate the properties of the surviving community in the stable phase. We find that the behaviour as a function of the model parameters is often similar to the pairwise model. For example, the presence of more exploitative interactions increases stability. However we also find differences. For instance, we confirm in more general settings an observation made previously in model with third-order interactions that more competition between species can increase linear stability, and the diversity in the community, an effect not seen in the pairwise model. The phase diagram of the model with higher-order interactions is more complex than that of the model with pairwise interactions. We identify a new mathematical condition for a sudden onset of diverging abundances.
A weather-driven mathematical model of Culex population abundance and the impact of vector control interventions
Authors: Suman Bhowmick, Patrick Irwin, Kristina Lopez, Megan Lindsay Fritz, Rebecca Lee Smith
Date: 2024-09-17 00:00:00

Even as the incidence of mosquito-borne diseases like West Nile Virus (WNV) in North America has risen over the past decade, effectively modelling mosquito population density or, the abundance has proven to be a persistent challenge. It is critical to capture the fluctuations in mosquito abundance across seasons in order to forecast the varying risk of disease transmission from one year to the next. We develop a process-based mechanistic weather-driven Ordinary Differential Equation (ODE) model to study the population biology of both aqueous and terrestrial stages of mosquito population. The progression of mosquito lifecycle through these stages is influenced by different factors, including temperature, daylight hours, intra-species competition and the availability of aquatic habitats. Weather-driven parameters are utilised in our work, are a combination of laboratory research and literature data. In our model, we include precipitation data as a substitute for evaluating additional mortality in the mosquito population. We compute the \textit{Basic offspring number} of the associated model and perform sensitivity analysis. Finally, we employ our model to assess the effectiveness of various adulticides strategies to predict the reduction in mosquito population. This enhancement in modelling of mosquito abundance can be instrumental in guiding interventions aimed at reducing mosquito populations and mitigating mosquito-borne diseases such as the WNV.