pwc-archive/papers-with-abstracts huggingface dataset:
Advanced safety filter based on SOS Control Barrier and Lyapunov Functions
Authors: Michael Schneeberger, Silvia Mastellone, Florian Dörfler
Date: 2024-01-12 00:00:00

This paper presents a novel safety filter framework that ensures both safety and the preservation of the legacy control action within a nominal region. This modular design allows the safety filter to be integrated into the control hierarchy without compromising the performance of the existing legacy controller within the nominal region. This is accomplished by formulating multiple Control Barrier Functions (CBFs) and Control Lyapunov-like Functions (CLFs) conditions, alongside a forward invariance condition for the legacy controller, as sum-of-squares constraints utilizing Putinar's Positivstellensatz. Additionally, the state-dependent inequality constraints of the resulting Quadratic Program -- encoding the CBF and CLF conditions -- are designed to remain inactive within the nominal region, ensuring perfect tracking of the legacy control action. Our safety filter design is also the first to include quadratic input constraints, and does not need an explicit specification of the attractor, as it is implicitly defined by the legacy controller. To avoid the chattering effect and guarantee the uniqueness and Lipschitz continuity of solutions, the state-dependent inequality constraints of the Quadratic Program are selected to be sufficiently regular. Finally, we demonstrate the method in a detailed case study involving the control of a three-phase ac/dc power converter.
The Role of Identification in Data-driven Policy Iteration: A System Theoretic Study
Authors: Bowen Song, Andrea Iannelli
Date: 2024-01-12 00:00:00

The goal of this article is to study fundamental mechanisms behind so-called indirect and direct data-driven control for unknown systems. Specifically, we consider policy iteration applied to the linear quadratic regulator problem. Two iterative procedures, where data collected from the system are repeatedly used to compute new estimates of the desired optimal controller, are considered. In indirect policy iteration, data are used to obtain an updated model estimate through a recursive identification scheme, which is used in a certainty-equivalent fashion to perform the classic policy iteration update. By casting the concurrent model identification and control design as a feedback interconnection between two algorithmic systems, we provide a closed-loop analysis that shows convergence and robustness properties for arbitrary levels of excitation in the data. In direct policy iteration, data are used to approximate the value function and design the associated controller without requiring the intermediate identification step. After proposing an extension to a recently proposed scheme that overcomes potential identifiability issues, we establish under which conditions this procedure is guaranteed to deliver the optimal controller. Based on these analyses we are able to compare the strengths and limitations of the two approaches, highlighting aspects such as the required samples, convergence properties, and excitation requirement. Simulations are also provided to illustrate the results.
LMI-based robust model predictive control for a quarter car with series active variable geometry suspension
Authors: Zilin Feng, Anastasis Georgiou, Simos A. Evangelou, Min Yu, Imad M Jaimoukha, Daniele Dini
Date: 2024-01-12 00:00:00
Tasks: Model Predictive Control

This paper proposes a robust model predictive control-based solution for the recently introduced series active variable geometry suspension (SAVGS) to improve the ride comfort and road holding of a quarter car. In order to close the gap between the nonlinear multi-body SAVGS model and its linear equivalent, a new uncertain system characterization is proposed that captures unmodeled dynamics, parameter variation, and external disturbances. Based on the newly proposed linear uncertain model for the quarter car SAVGS system, a constrained optimal control problem (OCP) is presented in the form of a linear matrix inequality (LMI) optimization. More specifically, utilizing semidefinite relaxation techniques a state-feedback robust model predictive control (RMPC) scheme is presented and integrated with the nonlinear multi-body SAVGS model, where state-feedback gain and control perturbation are computed online to optimise performance, while physical and design constraints are preserved. Numerical simulation results with different ISO-defined road events demonstrate the robustness and significant performance improvement in terms of ride comfort and road holding of the proposed approach, as compared to the conventional passive suspension, as well as, to actively controlled SAVGS by a previously developed conventional H-infinity control scheme.
Real-time MPC with Control Barrier Functions for Autonomous Driving using Safety Enhanced Collocation
Authors: Jean Pierre Allamaa, Panagiotis Patrinos, Toshiyuki Ohtsuka, Tong Duy Son
Date: 2024-01-12 00:00:00
Tasks: Autonomous Driving, Model Predictive Control

The autonomous driving industry is continuously dealing with safety-critical scenarios, and nonlinear model predictive control (NMPC) is a powerful control strategy for handling such situations. However, standard safety constraints are not scalable and require a long NMPC horizon. Moreover, the adoption of NMPC in the automotive industry is limited by the heavy computation of numerical optimization routines. To address those issues, this paper presents a real-time capable NMPC for automated driving in urban environments, using control barrier functions (CBFs). Furthermore, the designed NMPC is based on a novel collocation transcription approach, named RESAFE/COL, that allows to reduce the number of optimization variables while still guaranteeing the continuous time (nonlinear) inequality constraints satisfaction, through regional convex hull approximation. RESAFE/COL is proven to be 5 times faster than multiple shooting and more tractable for embedded hardware without a decrease in the performance, nor accuracy and safety of the numerical solution. We validate our NMPC-CBF with RESAFE/COL on digital twins of the vehicle and the urban environment and show the safe controller's ability to improve crash avoidance by 91\%. Supplementary visual material can be found at https://youtu.be/_EnbfYwljp4.
Bicycle Stabilization using mechanism optimization and Digital LQR
Authors: Reza Pirayeshshirazinezhad
Date: 2024-01-12 00:00:00

This study introduces lateral pendulum as an innovative balancer design for bicycle stabilization. This pendulum, operating in the bicycle's vertical plane, enables the bicycle to remain stationary. The paper develops a dynamic model for a bicycle equipped with this lateral pendulum, using Lagrange's method, where the equations are validated with ADAMS software. The stabilization is demonstrated with traditional vertical and novel lateral pendulums, managed through a genetic-pole placement control algorithm. This approach showcases the superiority of the lateral pendulum over traditional methods, including vertical pendulums and steering the handlebar. Additionally, a Digital Linear Quadratic Regulator controller is implemented for practical application, further enhancing system stability.
Joint Mechanical and Electrical Adjustment of IRS-aided LEO Satellite MIMO Communications
Authors: Doyoung Kim, Seongah Jeong
Date: 2024-01-12 00:00:00

In this correspondence, we propose a joint mechanical and electrical adjustment of intelligent reflecting surface (IRS) for the performance improvements of low-earth orbit (LEO) satellite multiple-input multiple-output (MIMO) communications. In particular, we construct a three-dimensional (3D) MIMO channel model for the mechanically-tilted IRS in general deployment, and consider two types of scenarios with and without the direct path of LEO-ground user link due to the orbital flight. With the aim of maximizing the end-to-end performance, we jointly optimize tilting angle and phase shift of IRS along with the transceiver beamforming, whose performance superiority is verified via simulations with the Orbcomm LEO satellite using a real orbit data.
Distributed Solvers for Network Linear Equations with Scalarized Compression
Authors: Lei Wang, Zihao Ren, Deming Yuan, Guodong Shi
Date: 2024-01-12 00:00:00
Tasks: Distributed Computing

Distributed computing is fundamental to multi-agent systems, with solving distributed linear equations as a typical example. In this paper, we study distributed solvers for network linear equations over a network with node-to-node communication messages compressed as scalar values. Our key idea lies in a dimension compression scheme that includes a dimension-compressing vector and a data unfolding step. The compression vector applies to individual node states as an inner product to generate a real-valued message for node communication. In the unfolding step, such scalar message is then plotted along the subspace generated by the compression vector for the local computations. We first present a compressed consensus flow that relies only on such scalarized communication, and show that linear convergence can be achieved with well excited signals for the compression vector. We then employ such a compressed consensus flow as a fundamental consensus subroutine to develop distributed continuous-time and discrete-time solvers for network linear equations, and prove their linear convergence properties under scalar node communications. With scalar communications, a direct benefit would be the reduced node-to-node communication channel burden for distributed computing. Numerical examples are presented to illustrate the effectiveness of the established theoretical results.
Model-Free Reinforcement Learning for Automated Fluid Administration in Critical Care
Authors: Elham Estiri, Hossein Mirinejad
Date: 2024-01-11 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

Fluid administration, also called fluid resuscitation, is a medical treatment to restore the lost blood volume and optimize cardiac functions in critical care scenarios such as burn, hemorrhage, and septic shock. Automated fluid administration systems (AFAS), a potential means to improve the treatment, employ computational control algorithms to automatically adjust optimal fluid infusion dosages by targeting physiological variables (e.g., blood volume or blood pressure). Most of the existing AFAS control algorithms are model-based approaches, and their performance is highly dependent on the model accuracy, making them less desirable in real-world care of critically ill patients due to complexity and variability of modeling patients physiological states. This work presents a novel model-free reinforcement learning (RL) approach for the control of fluid infusion dosages in AFAS systems. The proposed RL agent learns to adjust the blood volume to a desired value by choosing the optimal infusion dosages using a Q-learning algorithm. The RL agent learns the optimal actions by interacting with the environment (without having the knowledge of system dynamics). The proposed methodology (i) overcomes the need for a precise mathematical model in AFAS systems and (ii) provides a robust performance in rejecting clinical noises and reaching desired hemodynamic states, as will be shown by simulation results.
Multifractal organization of EEG signals in Multiple Sclerosis
Authors: Marcin Wątorek, Wojciech Tomczyk, Magda Gawłowska, Natalia Golonka-Afek, Aleksandra Żyrkowska, Monika Marona, Marcin Wnuk, Agnieszka Słowik, Jeremi K. Ochab, Magdalena Fafrowicz, Tadeusz Marek, Paweł Oświęcimka
Date: 2024-01-16 00:00:00
Tasks: EEG, Time Series

Quantifying the complex/multifractal organization of the brain signals is crucial to fully understanding the brain processes and structure. In this contribution, we performed the multifractal analysis of the electroencephalographic (EEG) data obtained from a controlled multiple sclerosis (MS) study, focusing on the correlation between the degree of multifractality, disease duration, and disability level. Our results reveal a significant correspondence between the complexity of the time series and multiple sclerosis development, quantified respectively by scaling exponents and the Expanded Disability Status Scale (EDSS). Namely, for some brain regions, a well-developed multifractality and little persistence of the time series were identified in patients with a high level of disability, whereas the control group and patients with low EDSS were characterised by persistence and monofractality of the signals. The analysis of the cross-correlations between EEG signals supported these results, with the most significant differences identified for patients with EDSS $> 1$ and the combined group of patients with EDSS $\leq 1$ and controls. No association between the multifractality and disease duration was observed, indicating that the multifractal organisation of the data is a hallmark of developing the disease. The observed complexity/multifractality of EEG signals is hypothetically a result of neuronal compensation -- i.e., of optimizing neural processes in the presence of structural brain degeneration. The presented study is highly relevant due to the multifractal formalism used to quantify complexity and due to scarce resting-state EEG evidence for cortical reorganization associated with compensation.
WISE: full-Waveform variational Inference via Subsurface Extensions
Authors: Ziyi Yin, Rafael Orozco, Mathias Louboutin, Felix J. Herrmann
Date: 2023-12-11 00:00:00
Tasks: Variational Inference

We introduce a probabilistic technique for full-waveform inversion, employing variational inference and conditional normalizing flows to quantify uncertainty in migration-velocity models and its impact on imaging. Our approach integrates generative artificial intelligence with physics-informed common-image gathers, reducing reliance on accurate initial velocity models. Considered case studies demonstrate its efficacy producing realizations of migration-velocity models conditioned by the data. These models are used to quantify amplitude and positioning effects during subsequent imaging.
Nanoantennas and Nanoradars: The Future of Integrated Sensing and Communication at the Nanoscale
Authors: M Javad Fakhimi, Ozgur B Akan
Date: 2024-01-14 00:00:00
Tasks: Integrated sensing and communication, ISAC

Nanoantennas, operating at optical frequencies, are a transformative technology with broad applications in 6G wireless communication, IoT, smart cities, healthcare, and medical imaging. This paper explores their fundamental aspects, applications, and advancements, aiming for a comprehensive understanding of their potential in various applications. It begins by investigating macroscopic and microscopic Maxwell's equations governing electromagnetic wave propagation at different scales. The study emphasizes the critical role of Surface Plasmon Polariton (SPP) wave propagation in enhancing light-matter interactions, contributing to high data rates, and enabling miniaturization. Additionally, it explores using two-dimensional materials like graphene for enhanced control in terahertz communication and sensing. The paper also introduces the employment of nanoantennas as the main building blocks of Nano-scale Radar (NR) systems for the first time in the literature. NRs, integrated with communication signals, promise accurate radar sensing for nanoparticles inside a nano-channel, making them a potential future application in integrated sensing and communication (ISAC) systems. These nano-scale radar systems detect and extract physical or electrical properties of nanoparticles through transmitting, receiving, and processing electromagnetic waves at ultra-high frequencies in the optical range. This task requires nanoantennas as transmitters/receivers/transceivers, sharing the same frequency band and hardware for high-performance sensing and resolution.
Exposure effects are not automatically useful for policymaking
Authors: Eric Auerbach, Jonathan Auerbach, Max Tabord-Meehan
Date: 2024-01-11 00:00:00

We thank Savje (2023) for a thought-provoking article and appreciate the opportunity to share our perspective as social scientists. In his article, Savje recommends misspecified exposure effects as a way to avoid strong assumptions about interference when analyzing the results of an experiment. In this invited discussion, we highlight a limiation of Savje's recommendation: exposure effects are not generally useful for evaluating social policies without the strong assumptions that Savje seeks to avoid.
A Note on Uncertainty Quantification for Maximum Likelihood Parameters Estimated with Heuristic Based Optimization Algorithms
Authors: Zachary Porreca
Date: 2024-01-13 00:00:00
Tasks: Uncertainty Quantification

Gradient-based solvers risk convergence to local optima, leading to incorrect researcher inference. Heuristic-based algorithms are able to ``break free" of these local optima to eventually converge to the true global optimum. However, given that they do not provide the gradient/Hessian needed to approximate the covariance matrix and that the significantly longer computational time they require for convergence likely precludes resampling procedures for inference, researchers often are unable to quantify uncertainty in the estimates they derive with these methods. This note presents a simple and relatively fast two-step procedure to estimate the covariance matrix for parameters estimated with these algorithms. This procedure relies on automatic differentiation, a computational means of calculating derivatives that is popular in machine learning applications. A brief empirical example demonstrates the advantages of this procedure relative to bootstrapping and shows the similarity in standard error estimates between this procedure and that which would normally accompany maximum likelihood estimation with a gradient-based algorithm.
Robust Analysis of Short Panels
Authors: Andrew Chesher, Adam M. Rosen, Yuanqi Zhang
Date: 2024-01-12 00:00:00

Many structural econometric models include latent variables on whose probability distributions one may wish to place minimal restrictions. Leading examples in panel data models are individual-specific variables sometimes treated as "fixed effects" and, in dynamic models, initial conditions. This paper presents a generally applicable method for characterizing sharp identified sets when models place no restrictions on the probability distribution of certain latent variables and no restrictions on their covariation with other variables. In our analysis latent variables on which restrictions are undesirable are removed, leading to econometric analysis robust to misspecification of restrictions on their distributions which are commonplace in the applied panel data literature. Endogenous explanatory variables are easily accommodated. Examples of application to some static and dynamic binary, ordered and multiple discrete choice and censored panel data models are presented.
Grassroots Innovation Actors: Their Role and Positioning in Economic Ecosystems -- A Comparative Study Through Complex Network Analysis
Authors: Marcelo S. Tedesco, Francisco Javier Ramos Soria
Date: 2023-12-22 00:00:00

This study offers an examination of grassroots innovation actors and their integration within larger economic ecosystems. Through a comparative analysis in Oaxaca, Mexico; La Plata, Argentina; and Araucania, Chile, this research sheds light on the vital role that grassroots innovation plays in broader economic ecosystems. Using Complex Network Analysis and the TE-SER model, the study unveils how these actors interact, collaborate, and influence major economic ecosystems in the context of complex social challenges. The findings highlight that actors from the grassroots innovation ecosystem make up a significant portion of the larger innovation-driven entrepreneurial economic ecosystem, accounting for between 20% and 30% in all three cases and are strategically positioned within the ecosystem's structural network. Additionally, this study emphasizes the potential for greater integration of grassroots innovation actors to leverage resources and foster socio-economic development. The research concludes by advocating for further studies in similar socio-economic contexts to enhance our understanding of integration dynamics and mutual benefits between grassroots innovation ecosystems and other larger economic systems.
Impermanent Loss Conditions: An Analysis of Decentralized Exchange Platforms
Authors: Matthias Hafner, Helmut Dietl
Date: 2024-01-15 00:00:00

Decentralized exchanges are widely used platforms for trading crypto assets. The most common types work with automated market makers (AMM), allowing traders to exchange assets without needing to find matching counterparties. Thereby, traders exchange against asset reserves managed by smart contracts. These assets are provided by liquidity providers in exchange for a fee. Static analysis shows that small price changes in one of the assets can result in losses for liquidity providers. Despite the success of AMMs, it is claimed that liquidity providers often suffer losses. However, the literature does not adequately consider the dynamic effects of fees over time. Therefore, we investigate the impermanent loss problem in a dynamic setting using Monte Carlo simulations. Our findings indicate that price changes do not necessarily lead to losses. Fees paid by traders and arbitrageurs are equally important. In this respect, we can show that an arbitrage-friendly environment benefits the liquidity provider. Thus, we suggest that AMM developers should promote an arbitrage-friendly environment rather than trying to prevent arbitrage.
Learning to be Homo Economicus: Can an LLM Learn Preferences from Choice
Authors: Jeongbin Kim, Matthew Kovach, Kyu-Min Lee, Euncheol Shin, Hector Tzavellas
Date: 2024-01-14 00:00:00

This paper explores the use of Large Language Models (LLMs) as decision aids, with a focus on their ability to learn preferences and provide personalized recommendations. To establish a baseline, we replicate standard economic experiments on choice under risk (Choi et al., 2007) with GPT, one of the most prominent LLMs, prompted to respond as (i) a human decision maker or (ii) a recommendation system for customers. With these baselines established, GPT is provided with a sample set of choices and prompted to make recommendations based on the provided data. From the data generated by GPT, we identify its (revealed) preferences and explore its ability to learn from data. Our analysis yields three results. First, GPT's choices are consistent with (expected) utility maximization theory. Second, GPT can align its recommendations with people's risk aversion, by recommending less risky portfolios to more risk-averse decision makers, highlighting GPT's potential as a personalized decision aid. Third, however, GPT demonstrates limited alignment when it comes to disappointment aversion.