pwc-archive/papers-with-abstracts huggingface dataset:
Angular Spread Statistics for 6.75 GHz FR1(C) and 16.95 GHz FR3 Mid-Band Frequencies in an Indoor Hotspot Environment
Authors: Dipankar Shakya, Mingjun Ying, Theodore S. Rappaport
Date: 2024-09-04 00:00:00

We present detailed multipath propagation spatial statistics for next-generation wireless systems operating at lower and upper mid-band frequencies spanning 6--24 GHz. The large-scale spatial characteristics of the wireless channel include Azimuth angular Spread of Departure (ASD) and Zenith angular Spread of Departure (ZSD) of multipath components (MPC) from a transmitter and the Azimuth angular Spread of Arrival (ASA) and Zenith angular Spread of Arrival (ZSA) at a receiver. The angular statistics calculated from measurements were compared with industry-standard 3GPP models, and ASD and ASA values were found to be in close agreement at both 6.75 GHz and 16.95 GHz. Measured LOS ASD was found larger than 3GPP ASD indicating more diverse MPC departure directions in the azimuth. ZSA and ZSD were observed smaller than the 3GPP modeling results as most multipath arrivals and departures during measurements were recorded at the boresight antenna elevation. The wide angular spreads indicate a multipath-rich spatial propagation at 6.75 GHz and 16.95 GHz, showing greater promise for the implementation of MIMO beamforming systems in the mid-band spectrum.
Adaptive Formation Learning Control for Cooperative AUVs under Complete Uncertainty
Authors: Emadodin Jandaghi, Mingxi Zhou, Paolo Stegagno, Chengzhi Yuan
Date: 2024-09-04 00:00:00

This paper presents a two-layer control framework for Autonomous Underwater Vehicles (AUVs) designed to handle uncertain nonlinear dynamics, including the mass matrix, previously assumed known. Unlike prior studies, this approach makes the controller independent of the robot's configuration and varying environmental conditions. The proposed framework applies across different environmental conditions affecting AUVs. It features a first-layer cooperative estimator and a second-layer decentralized deterministic learning controller. This architecture supports robust operation under diverse underwater scenarios, managing environmental effects like changes in water viscosity and flow, which impact the AUV's effective mass and damping dynamics. The first-layer estimator enables seamless inter-agent communication by sharing crucial system estimates without relying on global information. The second-layer controller uses local feedback to adjust each AUV's trajectory, ensuring accurate formation control and dynamic adaptability. Radial basis function neural networks enable local learning and knowledge storage, allowing AUVs to efficiently reapply learned dynamics after system restarts. Simulations validate the effectiveness of this framework, marking it as a significant advancement in distributed adaptive control systems for AUVs, enhancing operational flexibility and resilience in unpredictable marine environments.
A Variable Power Surface Error Function backstepping based Dynamic Surface Control of Non-Lower Triangular Nonlinear Systems
Authors: Abdulrazaq Nafiu Abubakar, Ali Nasir, MD Muzakkir Quamar
Date: 2024-09-04 00:00:00

A control design for error reduction in the tracking control for a class of non-lower triangular nonlinear systems is presented by combining techniques of Variable Power Surface Error Function (VPSEF), backstepping, and dynamic surface control. At each step of design, a surface error is obtained, and based on its magnitude, the VPSEF technique decides the surface error to be used. Thus, the backstepping-based virtual and actual control law is designed to stabilize the corresponding subsystem. To address the issue of circular structure, a first-order low-pass filter is used to handle the virtual control signal at each intermediate stage of the recursive design. The stability analysis of the closed-loop system demonstrates that all signals indicate semi-global uniform ultimate boundedness. Moreover, by using the switching strategy of the control input using the VPSEF technique suitably, it is possible to ensure that the steady-state tracking error converges to a neighborhood of zero with an arbitrarily very small size. The effectiveness of the proposed concept has been verified using two different simulated demonstrations.
Enhancing Information Freshness: An AoI Optimized Markov Decision Process Dedicated In the Underwater Task
Authors: Jingzehua Xu, Yimian Ding, Yiyuan Yang, Guanwen Xie, Shuai Zhang
Date: 2024-09-04 00:00:00
Tasks: Decision Making, Reinforcement Learning (RL)

Ocean exploration utilizing autonomous underwater vehicles (AUVs) via reinforcement learning (RL) has emerged as a significant research focus. However, underwater tasks have mostly failed due to the observation delay caused by acoustic communication in the Internet of underwater things. In this study, we present an AoI optimized Markov decision process (AoI-MDP) to improve the performance of underwater tasks. Specifically, AoI-MDP models observation delay as signal delay through statistical signal processing, and includes this delay as a new component in the state space. Additionally, we introduce wait time in the action space, and integrate AoI with reward functions to achieve joint optimization of information freshness and decision-making for AUVs leveraging RL for training. Finally, we apply this approach to the multi-AUV data collection task scenario as an example. Simulation results highlight the feasibility of AoI-MDP, which effectively minimizes AoI while showcasing superior performance in the task. To accelerate relevant research in this field, we have made the simulation codes available as open-source.
Neighbourhood conditions for network stability with link uncertainty
Authors: Simone Mariano, Michael Cantoni
Date: 2024-09-04 00:00:00

The main result relates to structured robust stability analysis of an input-output model for networks with link uncertainty. It constitutes a collection of integral quadratic constraints, which together imply robust stability of the uncertain networked dynamics. Each condition is decentralized in the sense that it depends on model data pertaining to the neighbourhood of a specific agent. By contrast, pre-existing conditions for the network model are link-wise decentralized, with each involving conservatively more localized problem data. A numerical example is presented to illustrate the advantage of the new broader neighbourhood conditions.
Comparative Analysis of Learning-Based Methods for Transient Stability Assessment
Authors: Xingjian Wu, Xiaoting Wang, Xiaozhe Wang, Peter E. Caines, Jingyu Liu
Date: 2024-09-03 00:00:00
Tasks: feature selection

Transient stability and critical clearing time (CCT) are important concepts in power system protection and control. This paper explores and compares various learning-based methods for predicting CCT under uncertainties arising from renewable generation, loads, and contingencies. Specially, we introduce new definitions of transient stability (B-stablilty) and CCT from an engineering perspective. For training the models, only the initial values of system variables and contingency cases are used as features, enabling the provision of protection information based on these initial values. To enhance efficiency, a hybrid feature selection strategy combining the maximal information coefficient (MIC) and Spearman's Correlation Coefficient (SCC) is employed to reduce the feature dimension. The performance of different learning-based models is evaluated on a WSCC 9-bus system.
Discrete-Time Maximum Likelihood Neural Distribution Steering
Authors: George Rapakoulias, Panagiotis Tsiotras
Date: 2024-09-03 00:00:00

This paper studies the problem of steering the distribution of a discrete-time dynamical system from an initial distribution to a target distribution in finite time. The formulation is fully nonlinear, allowing the use of general control policies, parametrized by neural networks. Although similar solutions have been explored in the continuous-time context, extending these techniques to systems with discrete dynamics is not trivial. The proposed algorithm results in a regularized maximum likelihood optimization problem, which is solved using machine learning techniques. After presenting the algorithm, we provide several numerical examples that illustrate the capabilities of the proposed method. We start from a simple problem that admits a solution through semidefinite programming, serving as a benchmark for the proposed approach. Then, we employ the framework in more general problems that cannot be solved using existing techniques, such as problems with non-Gaussian boundary distributions and non-linear dynamics.
Early Design Exploration of Aerospace Systems Using Assume-Guarantee Contracts
Authors: Nicolas Rouquette, Alessandro Pinto, Inigo Incer
Date: 2024-09-03 00:00:00
Tasks: Management

We present a compositional approach to early modeling and analysis of complex aerospace systems based on assume-guarantee contracts. Components in a system are abstracted into assume-guarantee specifications. Performing algebraic contract operations with Pacti allows us to relate local component specifications to that of the system. Applications to two aerospace case studies (the design of spacecraft to satisfy a rendezvous mission and the design of the thermal management system of a prototypical aircraft) show that this methodology provides engineers with an agile, early analysis and exploration process.
State and Action Factorization in Power Grids
Authors: Gianvito Losapio, Davide Beretta, Marco Mussi, Alberto Maria Metelli, Marcello Restelli
Date: 2024-09-03 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The increase of renewable energy generation towards the zero-emission target is making the problem of controlling power grids more and more challenging. The recent series of competitions Learning To Run a Power Network (L2RPN) have encouraged the use of Reinforcement Learning (RL) for the assistance of human dispatchers in operating power grids. All the solutions proposed so far severely restrict the action space and are based on a single agent acting on the entire grid or multiple independent agents acting at the substations level. In this work, we propose a domain-agnostic algorithm that estimates correlations between state and action components entirely based on data. Highly correlated state-action pairs are grouped together to create simpler, possibly independent subproblems that can lead to distinct learning processes with less computational and data requirements. The algorithm is validated on a power grid benchmark obtained with the Grid2Op simulator that has been used throughout the aforementioned competitions, showing that our algorithm is in line with domain-expert analysis. Based on these results, we lay a theoretically-grounded foundation for using distributed reinforcement learning in order to improve the existing solutions.
Adaptive Stochastic Predictive Control from Noisy Data: A Sampling-based Approach
Authors: Johannes Teutsch, Christopher Narr, Sebastian Kerz, Dirk Wollherr, Marion Leibold
Date: 2024-09-03 00:00:00

In this work, an adaptive predictive control scheme for linear systems with unknown parameters and bounded additive disturbances is proposed. In contrast to related adaptive control approaches that robustly consider the parametric uncertainty, the proposed method handles all uncertainties stochastically by employing an online adaptive sampling-based approximation of chance constraints. The approach requires initial data in the form of a short input-output trajectory and distributional knowledge of the disturbances. This prior knowledge is used to construct an initial set of data-consistent system parameters and a distribution that allows for sample generation. As new data stream in online, the set of consistent system parameters is adapted by exploiting set membership identification. Consequently, chance constraints are deterministically approximated using a probabilistic scaling approach by sampling from the set of system parameters. In combination with a robust constraint on the first predicted step, recursive feasibility of the proposed predictive controller and closed-loop constraint satisfaction are guaranteed. A numerical example demonstrates the efficacy of the proposed method.
Mixed Regular and Impulsive Sampled-data LQR
Authors: Jamal Daafouz, Jérôme Lohéac, Romain Postoyan
Date: 2024-09-03 00:00:00

We investigate the benefits of combining regular and impulsive inputs for the control of sampled-data linear time-invariant systems. We first observe that adding an impulsive term to a regular, zero-order-hold controller may help enlarging the set of sampling periods under which controllability is preserved by sampling. In this context, we provide a tailored Hautus-like necessary and sufficient condition under which controllability of the mixed regular, impulsive (MRI) sampled-data model is preserved. We then focus on LQR optimal control. After having presented the optimal controllers for the sampled-data LQR control in the MRI setting, we consider the scenario where an impulsive disturbance affects the dynamics and is known ahead of time. The solution to the so-called preview LQR is presented exploiting both regular and impulsive input components. Numerical examples, that include an insulin infusion benchmark, illustrate that leveraging both future disturbance information and MRI controls may lead to significant performance improvements.
COOCK project Smart Port 2025 D3.2: "Variability in Twinning Architectures"
Authors: Randy Paredis, Hans Vangheluwe, Pamela Adelino Ramos Albertins
Date: 2024-09-03 00:00:00

This document is a result of the COOCK project "Smart Port 2025: improving and accelerating the operational efficiency of a harbour eco-system through the application of intelligent technologies". The project is mainly aimed at SMEs, but also at large corporations. Together, they form the value-chain of the harbour. The digital maturity of these actors will be increased by model and data-driven digitization. The project brings together both technology users and providers/integrators. In this report, the broad spectrum of model and data-based digitization approaches is structured, under the unifying umbrella of "Digital Twins". During the (currently quite ad-hoc) digitization process and in particular, the creations of Digital Twins, a variety of choices have an impact on the ultimately realised system. This document identifies three stages during which this "variability" appears: the Problem Space Goal Construction Stage, the (Conceptual) Architecture Design Stage and the Deployment Stage. To illustrate the workflow, two simple use-cases are used: one of a ship moving in 1 dimension and, at a different scale and level of detail, a macroscopic model of the Port of Antwerp.
Hardware-Based Microgrid Coupled to Real-Time Simulated Power Grids for Evaluating New Control Strategies in Future Energy Systems
Authors: Michael Kyesswa, Friedrich Wiegel, Jan Wachter, Uwe Kühnapfel, Simon Waczowicz, Veit Hagenmeyer
Date: 2024-09-03 00:00:00

The design of new control strategies for future energy systems can neither be directly tested in real power grids nor be evaluated based on only current grid situations. In this regard, extensive tests are required in laboratory settings using real power system equipment. However, since it is impossible to replicate the entire grid section of interest, even in large-scale experiments, hardware setups must be supplemented by detailed simulations to reproduce the system under study fully. This paper presents a unique test environment in which a hardware-based microgrid environment is physically coupled with a large-scale real-time simulation framework. The setup combines the advantages of developing new solutions using hardware-based experiments and evaluating the impact on large-scale power systems using real-time simulations. In this paper, the interface between the microgrid-under-test environment and the real-time simulations is evaluated in terms of accuracy and communication delays. Furthermore, a test case is presented showing the approach's ability to test microgrid control strategies for supporting the grid. It is observed that the communication delays via the physical interface depend on the simulation sampling time and do not significantly affect the accuracy in the interaction between the hardware and the simulated grid.
Design and Operation Principles of a Wave-Controlled Reconfigurable Intelligent Surface
Authors: Gal Ben Itzhak, Miguel Saavedra-Melo, Benjamin Bradshaw, Ender Ayanoglu, Filippo Capolino, A. Lee Swindlehurst
Date: 2024-09-03 00:00:00

A Reflective Intelligent Surface (RIS) consists of many small reflective elements whose reflection properties can be adjusted to change the wireless propagation environment. Envisioned implementations require that each RIS element be connected to a controller, and as the number of RIS elements on a surface may be on the order of hundreds or more, the number of required electrical connectors creates a difficult wiring problem, especially at high frequencies where the physical space between the elements is limited. A potential solution to this problem was previously proposed by the authors in which "biasing transmission lines" carrying standing waves are sampled at each RIS location to produce the desired bias voltage for each RIS element. This solution has the potential to substantially reduce the complexity of the RIS control. This paper presents models for the RIS elements that account for mutual coupling and realistic varactor characteristics, as well as circuit models for sampling the transmission line to generate the RIS control signals. For the latter case, the paper investigates two techniques for conversion of the transmission line standing wave voltage to the varactor bias voltage, namely an envelope detector and a sample-and-hold circuit. The paper also develops a modal decomposition approach for generating standing waves that are able to generate beams and nulls in the resulting RIS radiation pattern that maximize either the Signal-to-Noise Ratio (SNR) or the Signal-to-Leakage-plus-Noise Ratio (SLNR). Extensive simulation results are provided for the two techniques, together with a discussion of computational complexity.
LiDAR-Aided Millimeter-Wave Range Extension using a Passive Mirror Reflector
Authors: Omar Ibrahim, Raj Sai Sohel Bandari, Mohammed E. Eltayeb
Date: 2024-09-03 00:00:00
Tasks: Position

Passive reflectors mitigate millimeter-wave (mmwave) link blockages by extending coverage to non-line-ofsight (NLoS) regions. However, their deployment often leads to irregular reflected beam patterns and coverage gaps. This results in rapid channel fluctuations and potential outages. In this paper, we propose two LiDAR-aided link enhancement techniques to address these challenges. Leveraging user position information, we introduce a location-dependent link control strategy and a user selection technique to improve NLoS link reliability and coverage. Experimental results validate the efficacy of the proposed techniques in reducing outages and enhancing NLoS signal strength.
Cooperative Global $\mathcal{K}$-exponential Tracking Control of Multiple Mobile Robots -- Extended Version
Authors: Liang Xu, Youfeng Su, He Cai
Date: 2024-09-03 00:00:00

This paper studies the cooperative tracking control problem for multiple mobile robots over a directed communication network. First, it is shown that the closed-loop system is uniformly globally asymptotically stable under the proposed distributed continuous feedback control law, where an explicit strict Lyapunov function is constructed. Then, by investigating the convergence rate, it is further proven that the closed-loop system is globally $\mathcal{K}$-exponentially stable. Moreover, to make the proposed control law more practical, the distributed continuous feedback control law is generalized to a distributed sampled-data feedback control law using the emulation approach, based on the strong integral input-to-state stable Lyapunov function. Numerical simulations are presented to validate the effectiveness of the proposed control methods.
Terminal Soft Landing Guidance Law Using Analytic Gravity Turn Trajectory
Authors: Seungyeop Han, Byeong-Un Jo, Koki Ho
Date: 2024-09-02 00:00:00
Tasks: Collision Avoidance

This paper presents an innovative terminal landing guidance law that utilizes an analytic solution derived from the gravity turn trajectory. The characteristics of the derived solution are thoroughly investigated, and the solution is employed to generate a reference velocity vector that satisfies terminal landing conditions. A nonlinear control law is applied to effectively track the reference velocity vector within a finite time, and its robustness against disturbances is studied. Furthermore, the guidance law is expanded to incorporate ground collision avoidance by considering the shape of the gravity turn trajectory. The proposed method's fuel efficiency, robustness, and practicality are demonstrated through comprehensive numerical simulations, and its performance is compared with existing methods.
An Investigation of Denial of Service Attacks on Autonomous Driving Software and Hardware in Operation
Authors: Tillmann Stübler, Andrea Amodei, Domenico Capriglione, Giuseppe Tomasso, Nicolas Bonnotte, Shawan Mohammed
Date: 2024-09-02 00:00:00
Tasks: Autonomous Driving

This research investigates the impact of Denial of Service (DoS) attacks, specifically Internet Control Message Protocol (ICMP) flood attacks, on Autonomous Driving (AD) systems, focusing on their control modules. Two experimental setups were created: the first involved an ICMP flood attack on a Raspberry Pi running an AD software stack, and the second examined the effects of single and double ICMP flood attacks on a Global Navigation Satellite System Real-Time Kinematic (GNSS-RTK) device for high-accuracy localization of an autonomous vehicle that is available on the market. The results indicate a moderate impact of DoS attacks on the AD stack, where the increase in median computation time was marginal, suggesting a degree of resilience to these types of attacks. In contrast, the GNSS device demonstrated significant vulnerability: during DoS attacks, the sample rate dropped drastically to approximately 50% and 5% of the nominal rate for single and double attacker configurations, respectively. Additionally, the longest observed time increments were in the range of seconds during the attacks. These results underscore the vulnerability of AD systems to DoS attacks and the critical need for robust cybersecurity measures. This work provides valuable insights into the design requirements of AD software stacks and highlights that external hardware and modules can be significant attack surfaces.
Analyzing electric vehicle, load and photovoltaic generation uncertainty using publicly available datasets
Authors: Md Umar Hashmi, Domenico Gioffrè, Simon Nagels, Dirk Van Hertem
Date: 2024-09-02 00:00:00

This paper aims to analyze three publicly available datasets for quantifying seasonal and annual uncertainty for efficient scenario creation. The datasets from Elaad, Elia and Fluvius are utilized to statistically analyze electric vehicle charging, normalized solar generation and low-voltage consumer load profiles, respectively. Frameworks for scenario generation are also provided for these datasets. The datasets for load profiles and solar generation analyzed are for the year 2022, thus embedding seasonal information. An online repository is created for the wider applicability of this work. Finally, the extreme load week(s) are identified and linked to the weather data measured at EnergyVille in Belgium.