pwc-archive/papers-with-abstracts huggingface dataset:
A Control Barrier Function Candidate for Quadrotors with Limited Field of View
Authors: Biagio Trimarchi, Fabrizio Schiano, Roberto Tron
Date: 2024-10-02 00:00:00

The problem of control based on vision measurements (bearings) has been amply studied in the literature; however, the problem of addressing the limits of the field of view of physical sensors has received relatively less attention (especially for agents with non-trivial dynamics). The technical challenge is that, as in most vision-based control approaches, a standard approach to the problem requires knowing the distance between cameras and observed features in the scene, which is not directly available. Instead, we present a solution based on a Control Barrier Function (CBF) approach that uses a splitting of the original differential constraint to effectively remove the dependence on the unknown measurement error. Compared to the current literature, our approach gives strong robustness guarantees against bounded distance estimation errors. We showcase the proposed solution with the numerical simulations of a double integrator and a quadrotor tracking a trajectory while keeping the corners of a rectangular gate in the camera field of view.
Sparse Actuation for LPV Systems with Full-State Feedback in $\mathcal{H}_2/\mathcal{H}_\infty$ Framework
Authors: Tanay Kumar, Raktim Bhattacharya
Date: 2024-10-01 00:00:00

This paper addresses the sparse actuation problem for nonlinear systems represented in the Linear Parameter-Varying (LPV) form. We propose a convex optimization framework that concurrently determines actuator magnitude limits and the state-feedback law that guarantees a user-specified closed-loop performance in the $\mathcal{H}_2/\mathcal{H}_\infty$ sense. We also demonstrate that sparse actuation is achieved when the actuator magnitude-limits are minimized in the $l_1$ sense. This is the first paper that addresses this problem for LPV systems. The formulation is demonstrated in a vibration control problem for a flexible wing.
Development of a Platform to Enable Real Time, Non-disruptive Testing and Early Fault Detection of Critical High Voltage Transformers and Switchgears in High Speed-rail
Authors: Jiawei Fan, Ming Zhu, Yingtao Jiang, Hualiang Teng
Date: 2024-10-01 00:00:00
Tasks: Fault Detection

Partial discharge (PD) incidents can occur in critical components of high-speed rail electric systems, such as transformers and switchgears, due to localized insulation defects that cannot withstand electric stress, leading to potential flashovers. These incidents can escalate over time, resulting in breakdowns, downtime, and safety risks. Fortunately, PD activities emit radio frequency (RF) signals, allowing for the development of a hardware platform for real-time, non-invasive PD detection and monitoring. The system uses an RF antenna and high-speed data acquisition to scan signals across a configurable frequency range (100MHz to 3GHz), utilizing intermediate frequency modulation and sliding frequency windows for detailed analysis. When signals exceed a threshold, the system records the events, capturing both raw signal data and spectrum snapshots. Real-time data is streamed to a cloud server, offering remote access through a dedicated smartphone application, enabling maintenance teams to monitor and respond promptly. Laboratory testing has confirmed the system's ability to accurately capture RF signals and provide real-time PD monitoring, enhancing the reliability and safety of high-speed rail infrastructure.
A Unified Approach for Optimal Cruise Airspeed with Variable Cost Index for Fuel-powered and All-electric Aircraft
Authors: Lucas Souza e Silva, Ali Akgunduz, Luis Rodrigues
Date: 2024-10-01 00:00:00
Tasks: All

This paper proposes for the first time a unified optimal approach to solve a direct operating cost (DOC) minimization problem where the cost index (CI) is time-varying. More specifically, the coefficient CI is modeled as a time-varying parameter commanded by Air Traffic Control (ATC). The proposed unified approach relies on the solution of an optimal control problem both for fuel-powered and all-electric aircraft. Furthermore, this paper demonstrates how a variable CI affects the solution of the optimization problem as it presents the equations that allow the computation of optimal constant cruise airspeed and flight time in response to step changes in the CI value. The proposed methodology is validated by a simulated flight scenario. In this scenario the inputs from the ATC are received during flight and the aircraft is required to adjust its optimal airspeed, flight time, and total energy consumption to comply with the operational restrictions imposed by the ATC. The optimal values of airspeed, flight time and energy consumption are computed for both a fuel-powered and an all-electric aircraft, thus enabling applications of the proposed approach to future air mobility all-electric vehicles.
Learning Chaotic Dynamics with Embedded Dissipativity
Authors: Sunbochen Tang, Themistoklis Sapsis, Navid Azizan
Date: 2024-10-01 00:00:00
Tasks: Sensitivity

Chaotic dynamics, commonly seen in weather systems and fluid turbulence, are characterized by their sensitivity to initial conditions, which makes accurate prediction challenging. Despite its sensitivity to initial perturbations, many chaotic systems observe dissipative behaviors and ergodicity. Therefore, recently various approaches have been proposed to develop data-driven models preserving invariant statistics over long horizons. Although these methods have shown empirical success in reducing instances of unbounded trajectory generation, many of the models are still prone to generating unbounded trajectories, leading to invalid statistics evaluation. In this paper, we propose a novel neural network architecture that simultaneously learns a dissipative dynamics emulator that guarantees to generate bounded trajectories and an energy-like function that governs the dissipative behavior. More specifically, by leveraging control-theoretic ideas, we derive algebraic conditions based on the learned energy-like function that ensure asymptotic convergence to an invariant level set. Using these algebraic conditions, our proposed model enforces dissipativity through a ReLU projection layer, which provides formal trajectory boundedness guarantees. Furthermore, the invariant level set provides an outer estimate for the strange attractor, which is known to be very difficult to characterize due to its complex geometry. We demonstrate the capability of our model in producing bounded long-horizon trajectory forecasts and characterizing the attractor for chaotic dynamical systems including Lorenz 96 and a truncated Kuramoto-Sivashinsky equation.
Outage-Constrained Sum Secrecy Rate Maximization for STAR-RIS with Energy-Harvesting Eavesdroppers
Authors: Zahra Rostamikafaki, Francois Chan, Claude D'Amours
Date: 2024-10-01 00:00:00

This article proposes a novel strategy for enhancing secure wireless communication through the use of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in a multiple-input single-output system. In the presence of energy-harvesting eavesdroppers, the study aims to maximize the secrecy rate while adhering to strict energy harvesting constraints. By dynamically manipulating the wireless environment with the STAR-RIS, the research examines the balance between harvested energy and secrecy rate under two key protocols: energy splitting and mode selection. The study addresses both imperfect and perfect channel state information (CSI) and formulates a complex non-convex optimization problem, which is solved using a penalty concave convex procedure combined with an alternating optimization algorithm. The method optimizes beamforming and STAR-RIS transmission and reflection coefficients to achieve a optimal balance between secure communication and energy harvesting constraints. Numerical simulations show that the proposed approach is effective, even with imperfect CSI, and outperforms conventional RIS methods in terms of robust security and energy performance.
Optimized Excitation Signal Design Employing Receding Horizon Control
Authors: Max Heinz Herkersdorf, Oliver Nelles
Date: 2024-10-01 00:00:00

A novel excitation signal design strategy based on a receding horizon control inspired optimization is presented. The proposed method is shown to effectively generate space-filling designs within the input space of a nonlinear dynamic process, thereby enabling sophisticated acquisition of information in previously unexplored operational areas. Additionally, the strategy can intensify the exploitation of specific operational areas during information gathering, offering flexibility in meeting application-specific requirements.
Optimized Excitation Signal Tailored to Pertinent Dynamic Process Characteristics
Authors: Max Heinz Herkersdorf, Tarek Koesters, Oliver Nelles
Date: 2024-10-01 00:00:00

The effectiveness of data-driven techniques significantly relies on the input signal used to generate the training data. Nevertheless, there is a notable gap in research when it comes to designing excitation signals for identifying nonlinear dynamic systems, likely because of the challenges involved. Based on current knowledge, it is crucial for excitation signals to effectively capture the nonlinearity across the entire operational area and to gather insights into the area-specific dynamic process characteristics. The Incremental Dynamic Space-Filling Design (IDS-FID) strategy designs excitation signals to achieve a space-filling distribution across the input space of a nonlinear approximator used in external dynamics modeling, gathering information throughout its operational area. Simultaneously, the approach enables for a heightened focus on either the systems steady-state or transient responses during information acquisition by altering the excitation signals dynamics, facilitating targeted insights into dynamic process characteristics.
Absolute centrality in a signed Friedkin-Johnsen based model: a graphical characterisation of influence
Authors: Aashi Shrinate, Twinkle Tripathy
Date: 2024-10-01 00:00:00

This paper studies the evolution of opinions governed by a Friedkin Johnsen (FJ) based model in arbitrary network structures with signed interactions. The agents contributing to the opinion formation are characterised as being influential. Initially, the agents are classified as opinion leaders and followers based on network connectivity and the nature of interactions. However, the addition of stubbornness leads to interesting behaviours wherein a non influential agent can now become influential and vice versa. Thereafter, a signal flow graph (SFG) based method is proposed to quantify the influence of an influential agents' opinions. Additionally, it helps illustrate the role played by network topology in shaping the final opinions of the agents. Based on this analysis, the absolute centrality measure is proposed to determine the overall influence of all the agents in the network. Unlike most of the existing measures, it is applicable to any network structure and considers the effect of stubbornness and antagonism. Examples are presented throughout the paper to illustrate and validate these results.
Interleaved One-Shot SPS Performance under Smart DoS Attacks in C-V2X Networks
Authors: Zepei Sun, Randall Berry
Date: 2024-10-01 00:00:00
Tasks: Scheduling

This paper evaluates the performance of the one-shot Semi-Persistent Scheduling (SPS) mechanism in Cellular Vehicle-to-Everything (C-V2X) networks under Denial-of-Service (DoS) smart attack scenarios. The study focuses on the impact of these attacks on key performance metrics, including Packet Delivery Ratio (PDR), Inter-Packet Gap (IPG), and Age of Information (AoI). Through extensive Monte Carlo simulations, we demonstrate that the one-shot mechanism significantly enhances network resilience by mitigating the adverse effects of smart DoS attacks. The findings reveal that while the one-shot mechanism improves the PDR and reduces the IPG and AoI tail values, its effectiveness diminishes slightly in high-density vehicular environments. Nevertheless, the one-shot mechanism proves to be a robust solution for maintaining the stability and reliability of C-V2X communications under adversarial conditions.
Quantifying the Dunkelflaute: An analysis of variable renewable energy droughts in Europe
Authors: Martin Kittel, Wolf-Peter Schill
Date: 2024-09-30 00:00:00

Variable renewable energy droughts, also referred to as "Dunkelflaute", emerge as a challenge for realizing climate-neutral energy systems based on variable wind and solar power. Using data on 38 historic weather years and an advanced identification method, we characterize European drought events for on- and offshore wind power, solar photovoltaics, and policy-relevant renewable technology portfolios. We show that drought characteristics heavily depend on the chosen threshold. Using single thresholds, as common in the literature, is thus not advisable. Applying a multi-threshold framework, we quantify how the complementarity of wind and solar power temporally and spatially alleviates drought frequency, duration, and severity within (portfolio effect) and across countries (balancing effect). We further identify the most extreme droughts and show how these drive major discharging periods of long-duration storage in a fully renewable European energy system. Such events comprise sequences of shorter, contiguous droughts of varying severity. In a perfectly interconnected Europe, the most extreme drought event occurred in winter 1996/97 and lasted 55~days. Yet, the average renewable portfolio availability during this event was still 47% of its long-run mean. As extreme droughts may span across the turn of years, single calendar year planning horizons are not suitable for modeling weather-resilient future energy scenarios.
Koopman Operator in the Weighted Function Spaces and its Learning for the Estimation of Lyapunov and Zubov Functions
Authors: Wentao Tang
Date: 2024-09-30 00:00:00

The mathematical properties and data-driven learning of the Koopman operator, which represents nonlinear dynamics as a linear mapping on a properly defined functional spaces, have become key problems in nonlinear system identification and control. However, Koopman operators that are approximately learned from snapshot data may not always accurately predict the system evolution on long horizons. In this work, by defining the Koopman operator on a space of weighted continuous functions and learning it on a weighted reproducing kernel Hilbert space, the Koopman operator is guaranteed to be contractive and the accumulation learning error is bounded. The weighting function, assumed to be known a priori, has an exponential decay with the flow or decays exponentially when compensated by an exponential factor. Under such a construction, the Koopman operator learned from data is used to estimate (i) Lyapunov functions for globally asymptotically stable dynamics, and (ii) Zubov-Lyapunov functions that characterize the domain of attraction. For these estimations, probabilistic bounds on the errors are derived.
A Data-Driven Approach To Preserve Safety and Reference Tracking for Constrained Cyber-Physical Systems Under Network Attacks
Authors: Mehran Attar, Walter Lucia
Date: 2024-09-30 00:00:00

This paper proposes a worst-case data-driven control architecture capable of ensuring the safety of constrained Cyber-Physical Systems under cyber-attacks while minimizing, whenever possible, potential degradation in tracking performance. To this end, a data-driven robust anomaly detector is designed to detect cyber-attack occurrences. Moreover, an add-on tracking supervisor module allows safe open-loop tracking control operations in case of unreliable measurements. On the plant side, a safety verification module and a local emergency controller are designed to manage severe attack scenarios that cannot be handled on the controller's side. These two modules resort to worst-case reachability and controllability data-driven arguments to detect potential unsafe scenarios and replace, whenever strictly needed, the tracking controller with emergency actions whose objective is to steer the plant's state trajectory in a predefined set of admissible and safe robust control invariant region until an attack-free scenario is restored. The effectiveness of the proposed solution has been shown through a simulation example.
Quantifying Metrics for Wildfire Ignition Risk from Geographic Data in Power Shutoff Decision-Making
Authors: Ryan Piansky, Sofia Taylor, Noah Rhodes, Daniel K. Molzahn, Line A. Roald, Jean-Paul Watson
Date: 2024-09-30 00:00:00
Tasks: Decision Making

Faults on power lines and other electric equipment are known to cause wildfire ignitions. To mitigate the threat of wildfire ignitions from electric power infrastructure, many utilities preemptively de-energize power lines, which may result in power shutoffs. Data regarding wildfire ignition risks are key inputs for effective planning of power line de-energizations. However, there are multiple ways to formulate risk metrics that spatially aggregate wildfire risk map data, and there are different ways of leveraging this data to make decisions. The key contribution of this paper is to define and compare the results of employing six metrics for quantifying the wildfire ignition risks of power lines from risk maps, considering both threshold- and optimization-based methods for planning power line de-energizations. The numeric results use the California Test System (CATS), a large-scale synthetic grid model with power line corridors accurately representing California infrastructure, in combination with real Wildland Fire Potential Index data for a full year. This is the first application of optimal power shutoff planning on such a large and realistic test case. Our results show that the choice of risk metric significantly impacts the lines that are de-energized and the resulting load shed. We find that the optimization-based method results in significantly less load shed than the threshold-based method while achieving the same risk reduction.
Simple controller design to achieve iso-damping robustness: Non-iterative data-driven approach based on fractional-order reference model
Authors: Ansei Yonezawa, Heisei Yonezawa, Shuichi Yahagi, Itsuro Kajiwara
Date: 2024-09-30 00:00:00

This study proposes a simple controller design approach to achieve a class of robustness, the so-called iso-damping property. The proposed approach can be executed using only one-shot input/output data. An accurate mathematical model of a controlled plant is not required. The model-reference control problem is defined to achieve the desired closed-loop specifications, including the iso-damping, and the reference model is designed on the basis of fractional-order calculus. The optimization problem for the model-reference control is formulated using the one-shot input/output data while considering the bounded-input bounded-output (BIBO) stability from a bounded reference input to a bounded output. The iso-damping robust controller is obtained by solving the optimization problem. The representative advantages of the proposed approach over the conventional methods are the simplicity, practicality, and reliability from the viewpoint of the unnecessity of the plant model and explicit consideration of the BIBO stability from a bounded reference input to a bounded output. Numerical examples demonstrate the validity of the proposed approach.
Design and validation of a fuzzy logic controller for multi-section continuum robots
Authors: Jing Liu, Tianyi Zeng, Abdelkhalick Mohammad, Xin Dong, Dragos Axinte
Date: 2024-09-30 00:00:00
Tasks: Navigate

The rise of multi-section continuum robots (CRs) has captivated researchers and practitioners across diverse industries and medical fields. Accurate modeling of these dexterous manipulators continues to be a significant challenge. This complexity stems primarily from many nonlinearities that plague their behavior, including hysteresis and cable elongation. Researchers have devised a spectrum of model-based and learning-based strategies to navigate this intricate landscape, aiming to conquer the modeling problem and elevate control performance. Despite the advancements in these approaches, they encounter challenges stemming from their complex design and intricate learning processes, impairing versatility and hindering robust closed-loop control. This paper introduces a simple-structured, model-less fuzzy logic controller for the closed-loop control of continuum robots. Unlike traditional methods relying on complex models and numerous sensors, this controller boasts a built-in shape reconstruction algorithm. This algorithm allows it to achieve robust control using only the feedback of end position and orientation, significantly reducing sensor dependence. It efficiently adapts to various nonlinearities like hysteresis, cable elongation, and unexpected external disturbances. The experimental results conclusively demonstrate the accuracy and robustness of the proposed fuzzy controller. On a three-section, six-degree-of-freedom continuum robot, it achieved a miniscule trajectory tracking Root Mean Square Error (RMSE) from 0.28 to 0.54 mm, representing just 0.17 to 0.32% of the robot's length. Additionally, the controller demonstrates robustness by successfully handling an unexpected external disturbance of 100g during the trajectory tracking.
Advanced Resilience Planning for Distribution Systems
Authors: Ahmad Bin Afzal, Nabil Mohammed, Shehab Ahmed, Charalambos Konstantinou
Date: 2024-09-30 00:00:00

Climate change has led to an increase in the frequency and severity of extreme weather events, posing significant challenges for power distribution systems. In response, this work presents a planning approach in order to enhance the resilience of distribution systems against climatic hazards. The framework systematically addresses uncertainties during extreme events, including weather variability and line damage. Key strategies include line hardening, backup diesel generators, and sectionalizers to strengthen resilience. We model spatio-temporal dynamics and costs through a hybrid model integrating stochastic processes with deterministic elements. A two-stage stochastic mixed-integer linear approach is developed to optimize resilience investments against load loss, generator operations, and repairs. Case studies on the IEEE 15-bus benchmark system and a realistic distribution grid model in Riyadh, Saudi Arabia demonstrate enhanced system robustness as well as cost efficiency of 10% and 15%, respectively.
Spacecraft Attitude Control Under Reaction Wheel Constraints Using Control Lyapunov and Control Barrier Functions
Authors: Milad Alipour Shahraki, Laurent Lessard
Date: 2024-09-30 00:00:00

This paper introduces a novel control strategy for agile spacecraft attitude control, addressing reaction wheel-related input and state constraints. An optimal-decay control Lyapunov function quadratic program stabilizes the system and mitigates chattering at low sampling frequencies, while control barrier functions enforce hard state constraints. Numerical simulations validate the method's practicality and efficiency for real-time agile spacecraft attitude control.